{ "cells": [ { "cell_type": "code", "execution_count": 17, "id": "61cefd57", "metadata": {}, "outputs": [], "source": [ "import netCDF4 as nc\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "from datetime import datetime, timedelta\n", "from netCDF4 import Dataset\n", "from reportlab.lib.pagesizes import letter\n", "from reportlab.pdfgen import canvas\n", "from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle\n", "from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer\n", "import matplotlib.dates as mdates\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "155197c5", "metadata": {}, "outputs": [], "source": [ "# Constants\n", "C_p = 1005 # Specific heat capacity of dry air at constant pressure (J/kg/K)\n", "g = 9.81 # Gravitational acceleration (m/s^2)\n", "Ttrip = 273.16 # Triple point temperature in Kelvin\n", "Rd=287.04\n", "Rv=461.5\n", "epsilon=(Rv/Rd)-1" ] }, { "cell_type": "code", "execution_count": 3, "id": "541bc008", "metadata": {}, "outputs": [], "source": [ "# Function to calculate pressure at heights\n", "def calculate_pressure_at_heights(df):\n", " calculated_pressures = [] # To store pressure values for all times and altitudes\n", " \n", " # Loop through each row in the dataframe, accessing each profile (altitudes and temperature)\n", " for idx, row in df.iterrows():\n", " # Extract the altitude and temperature profiles for this timestamp\n", " temperature_altitudes = row['temperature_altitudes']\n", " temperature_profile = row['temperature_profile']\n", " surface_pressure = row['surf_pres'] # Surface pressure for this timestamp\n", " \n", " # Ensure that the altitudes are sorted, just in case\n", " sorted_indices = np.argsort(temperature_altitudes)\n", " sorted_altitudes = np.array(temperature_altitudes)[sorted_indices]\n", " sorted_temperatures = np.array(temperature_profile)[sorted_indices]\n", " \n", " # Start with the surface pressure (P0)\n", " P0 = surface_pressure # Surface pressure (hPa)\n", " pressures = [P0] # First pressure is the surface pressure\n", "\n", " # Perform second-order (Trapezoidal Rule) integration for each altitude step\n", " for i in range(1, len(sorted_altitudes)):\n", " z1 = sorted_altitudes[i - 1]\n", " z2 = sorted_altitudes[i]\n", " T1 = sorted_temperatures[i - 1]\n", " T2 = sorted_temperatures[i]\n", " P1 = pressures[-1] # Previous pressure value\n", " \n", " delta_z = z2 - z1 # Altitude difference\n", " \n", " # First order estimate of P2 based on P1\n", " P2_guess = P1 * np.exp(-g * delta_z / (Rd * T1))\n", " \n", " # Trapezoidal rule correction for P2\n", " delta_P = 0.5 * (-(g * P1 / (Rd * T1)) + -(g * P2_guess / (Rd * T2))) * delta_z\n", " P2 = P1 + delta_P\n", " pressures.append(P2)\n", " \n", " # Append the calculated pressures for this profile\n", " calculated_pressures.append(pressures)\n", " \n", " return calculated_pressures\n", "\n", "# Function to calculate saturation vapor pressure\n", "def calculate_saturation_vapor_pressure(T):\n", " es = 610.78 * np.exp(17.2694 * (T - Ttrip) / (T - 35.86))\n", " return es\n", "\n", "# Function to calculate specific humidity (qv)\n", "def calculate_specific_humidity(ev, p):\n", " return ev * 1000 / (p * 100 + (((Rv / Rd) - 1) * (p * 100 - ev)))\n", "\n", "\n", "# Function to calculate potential temperature (theta) based on current pressure level and surface pressure\n", "def calculate_potential_temperature(T, P_i, P_0):\n", " \"\"\"\n", " Calculate potential temperature (theta) given temperature T (K) and pressures at level P_i (hPa) and surface pressure P_0 (hPa).\n", " \"\"\"\n", " return T * (P_0 / P_i) ** (Rd / C_p)\n", "\n", "# Function to calculate theta_v for each altitude level\n", "def calculate_theta_v_alternative_by_altitude(row):\n", " theta_v_profile = [] # Store results for this timestamp\n", " temperature_profile = row['temperature_profile'] # Temperature profile for this timestamp\n", " calculated_pressures = row['calculated_pressures'] # Pressures for this timestamp\n", " specific_humidity = row['specific_humidity'] # Specific humidity for this timestamp\n", " \n", " for i in range(len(temperature_profile)):\n", " T = temperature_profile[i] # Temperature at current altitude (K)\n", " P_i = calculated_pressures[i] # Pressure at current altitude (hPa)\n", " qv = specific_humidity[i] # Specific humidity at current altitude (kg/kg)\n", " \n", " if i == 0: # Surface level\n", " P_0 = row['surf_pres'] # Surface pressure (hPa)\n", " theta = T # Potential temperature at the surface is just T\n", " else:\n", " P_0 = row['surf_pres'] # Surface pressure remains constant\n", " theta = calculate_potential_temperature(T, P_i, P_0) # Potential temperature at altitude\n", " \n", " # Calculate virtual potential temperature (theta_v) for this altitude\n", " theta_v_alt = theta * (1 + (epsilon * qv/1000)) # qv is in kg/kg\n", " \n", " # Append the calculated theta_v to the profile list\n", " theta_v_profile.append(theta_v_alt)\n", " \n", " return theta_v_profile" ] }, { "cell_type": "code", "execution_count": 4, "id": "930017a9", "metadata": {}, "outputs": [], "source": [ "# Open the NetCDF file\n", "#file_path = 'C:\\\\Users\\\\magda\\\\Master Thesis\\\\Cloud Radar\\\\May 3rd\\\\240503_000002_P00_ZEN.LV0.NC'\n", "file_path = 'C:\\\\Users\\\\magda\\\\Master_Thesis\\\\Cloud_radar\\\\2024-05\\\\2024-05-03\\\\240503_000002_P00_ZEN.LV0.NC'\n", "dataset = nc.Dataset(file_path, mode='r')\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "0693e20f", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NetCDF Dataset Information:\n", "\n", "root group (NETCDF4 data model, file format HDF5):\n", " Program name: Doppler 3 sec\n", " Customer: DELFT 1\n", " dimensions(sizes): TAlt(93), HAlt(93), Chirp(3), C1Range(20), C1Vel(512), C2Range(53), C2Vel(512), C3Range(265), C3Vel(256), Time(1220)\n", " variables(dimensions): int32 ProgNo(), int32 ModelNo(), float32 Freq(), float32 AntSep(), float32 AntDia(), float32 AntG(), float32 HPBW(), float32 RadarConst(), |S1 DualPol(), |S1 CompEna(), |S1 AntiAlias(), float32 SampDur(), float32 GPSLat(), float32 GPSLon(), int32 CallInt(), float32 TAlts(TAlt), float32 HAlts(HAlt), int32 ChirpNum(), int32 DoppLen(Chirp), int32 AvgNum(Chirp), float32 SeqIntTime(Chirp), float32 RangeRes(Chirp), float32 MaxVel(Chirp), float32 ChanBW(Chirp), int32 ChirpLowIF(Chirp), int32 ChirpHighIF(Chirp), int32 ChirpFFTSize(Chirp), int32 ChirpInvSamp(Chirp), float32 ChirpCenterFreq(Chirp), float32 ChirpBW(Chirp), int32 FFTStartInd(Chirp), int32 FFTStopInd(Chirp), int32 ChirpFFTNo(Chirp), int32 SampRate(), int32 MaxRange(), |S1 SupPowLev(), |S1 SpkFilEna(), |S1 PhaseCor(), |S1 RelPowCor(), |S1 FFTWin(), uint16 FFTInRng(), uint16 SWVersion(), float32 NoiseFilt(), float32 CalibrOffset(), uint32 Time(Time), int32 Timems(Time), |S1 QualFlag(Time), float32 Rain(Time), float32 SurfRelHum(Time), float32 SurfTemp(Time), float32 SurfPres(Time), float32 SurfWS(Time), float32 SurfWD(Time), float32 DDVolt(Time), float32 DDTb(Time), float32 LWP(Time), float32 PowIF(Time), float32 Elv(Time), float32 Azm(Time), float32 Status(Time), float32 TPow(Time), float32 TTemp(Time), float32 RTemp(Time), float32 PCTemp(Time), float32 Res_1(Time), float32 Res_2(Time), float32 Res_3(Time), float32 TProf(Time, TAlt), float32 AHProf(Time, HAlt), float32 RHProf(Time, HAlt), float32 C1PNv(Time, C1Range), float32 C1SLv(Time, C1Range), float32 C1PNh(Time, C1Range), float32 C1SLh(Time, C1Range), float32 C2PNv(Time, C2Range), float32 C2SLv(Time, C2Range), float32 C2PNh(Time, C2Range), float32 C2SLh(Time, C2Range), float32 C3PNv(Time, C3Range), float32 C3SLv(Time, C3Range), float32 C3PNh(Time, C3Range), float32 C3SLh(Time, C3Range), int32 C1Fr(C1Range), float32 C1Range(C1Range), int32 C2Fr(C2Range), float32 C2Range(C2Range), int32 C3Fr(C3Range), float32 C3Range(C3Range), float32 C1VNoisePow(Time, C1Range), float32 C1HNoisePow(Time, C1Range), float32 C2VNoisePow(Time, C2Range), float32 C2HNoisePow(Time, C2Range), float32 C3VNoisePow(Time, C3Range), float32 C3HNoisePow(Time, C3Range), float32 C1VSpec(Time, C1Range, C1Vel), float32 C1HSpec(Time, C1Range, C1Vel), float32 C1ReVHSpec(Time, C1Range, C1Vel), float32 C1ImVHSpec(Time, C1Range, C1Vel), float32 C2VSpec(Time, C2Range, C2Vel), float32 C2HSpec(Time, C2Range, C2Vel), float32 C2ReVHSpec(Time, C2Range, C2Vel), float32 C2ImVHSpec(Time, C2Range, C2Vel), float32 C3VSpec(Time, C3Range, C3Vel), float32 C3HSpec(Time, C3Range, C3Vel), float32 C3ReVHSpec(Time, C3Range, C3Vel), float32 C3ImVHSpec(Time, C3Range, C3Vel)\n", " groups: \n", "\n", "Global Attributes:\n", "Program name: Doppler 3 sec\n", "Customer: DELFT 1\n", "\n", "Dimensions:\n", "TAlt: 93\n", "HAlt: 93\n", "Chirp: 3\n", "C1Range: 20\n", "C1Vel: 512\n", "C2Range: 53\n", "C2Vel: 512\n", "C3Range: 265\n", "C3Vel: 256\n", "Time: 1220\n", "\n", "Variables:\n", "\n", "Variable Name: ProgNo\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: int32\n", " Name: Chirp generator program number\n", "\n", "Variable Name: ModelNo\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: int32\n", " Name: Radar configuration: 0 - 94 GHz single pol, 1 - 94 GHz STSR, 2 - 94 GHz LDR\n", "\n", "Variable Name: Freq\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Carrier frequency of the radar active channel\n", " Units: GHz\n", "\n", "Variable Name: AntSep\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Distance between antenna axes\n", " Units: m\n", "\n", "Variable Name: AntDia\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Antenna diameter\n", " Units: m\n", "\n", "Variable Name: AntG\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Antenna gain\n", " Units: linear\n", "\n", "Variable Name: HPBW\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Antenna one way half power beam width\n", " Units: deg\n", "\n", "Variable Name: RadarConst\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Radar constant. For detailes see eq. 2.1.5 of the radar manual\n", " Units: linear\n", "\n", "Variable Name: DualPol\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: |S1\n", " Name: Polarimetric configuration. 0: Single pol, 1: LDR mode, 2: STSR mode\n", "\n", "Variable Name: CompEna\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: |S1\n", " Name: Spectral compression flag. 0: not compressed, 1: spectra compressed, 2: spectra compressed and spectral polarimetric variables are stored in the file\n", "\n", "Variable Name: AntiAlias\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: |S1\n", " Name: Antialiasing status. 0 - Spectra are not antialiased, 1 - Spectra have been antialiased.\n", "\n", "Variable Name: SampDur\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Sample duration\n", " Units: s\n", "\n", "Variable Name: GPSLat\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: GPS latitude\n", " Units: deg\n", "\n", "Variable Name: GPSLon\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: GPS longitude\n", " Units: deg\n", "\n", "Variable Name: CallInt\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: int32\n", " Name: Time period for automatic zero calibrations\n", " Units: Number of samples\n", "\n", "Variable Name: TAlts\n", "Dimensions: ('TAlt',)\n", "Shape: (93,)\n", "Data Type: float32\n", " Name: Temperature profile altitude layers\n", " Units: m\n", "\n", "Variable Name: HAlts\n", "Dimensions: ('HAlt',)\n", "Shape: (93,)\n", "Data Type: float32\n", " Name: Humidity profile altitude layers\n", " Units: m\n", "\n", "Variable Name: ChirpNum\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: int32\n", " Name: Number of chirp sequences\n", "\n", "Variable Name: DoppLen\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", " Name: Number of spectral lines in Doppler spectra\n", "\n", "Variable Name: AvgNum\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", " Name: Number of chirps averaged (coherently and non-coherently) for a single time sample\n", "\n", "Variable Name: SeqIntTime\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: float32\n", " Name: Effective integration time\n", " Units: s\n", "\n", "Variable Name: RangeRes\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: float32\n", " Name: Range resolution\n", " Units: m\n", "\n", "Variable Name: MaxVel\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: float32\n", " Name: Unambiguous Doppler velocity (+/-)\n", " Units: m/s\n", "\n", "Variable Name: ChanBW\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: float32\n", " Name: Bandwidth of individual radar channel (range gate)\n", " Units: Hz\n", "\n", "Variable Name: ChirpLowIF\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", " Name: Lowest IF frequency\n", " Units: Hz\n", "\n", "Variable Name: ChirpHighIF\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", " Name: Highest IF frequency\n", " Units: Hz\n", "\n", "Variable Name: ChirpFFTSize\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", " Name: Ranging FFT size\n", "\n", "Variable Name: ChirpInvSamp\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", " Name: Number of invalid samples at the beginning of chirps\n", "\n", "Variable Name: ChirpCenterFreq\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: float32\n", " Name: Chirp centre frequency at radar transmitter output\n", " Units: MHz\n", "\n", "Variable Name: ChirpBW\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: float32\n", "\n", "Variable Name: FFTStartInd\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", " Name: Start index in ranging FFT\n", "\n", "Variable Name: FFTStopInd\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", " Name: Last index in ranging FFT\n", "\n", "Variable Name: ChirpFFTNo\n", "Dimensions: ('Chirp',)\n", "Shape: (3,)\n", "Data Type: int32\n", "\n", "Variable Name: SampRate\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: int32\n", " Name: ADC sampling rate\n", " Units: Hz\n", "\n", "Variable Name: MaxRange\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: int32\n", " Name: Maximum unambiguous range\n", " Units: m\n", "\n", "Variable Name: SupPowLev\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: |S1\n", " Name: Flag indicating, if power leveling has been used. 0 - yes, 1 - no\n", "\n", "Variable Name: SpkFilEna\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: |S1\n", " Name: Flag indicating, if spike/plankton filter has been used. 0 - yes, 1 - no\n", "\n", "Variable Name: PhaseCor\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: |S1\n", " Name: Flag indicating, if differential phase correction has been used (only for dual pol radars). 0 - yes, 1 - no\n", "\n", "Variable Name: RelPowCor\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: |S1\n", " Name: Flag indicating, if relative power correction has been used (only for dual pol radars). 0 - yes, 1 - no\n", "\n", "Variable Name: FFTWin\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: |S1\n", " Name: FFT windows in use (for both ranging and Doppler FFT). 0 - Square, 1 - Parzen, 2 - Blackman, 3 - Welch, 4 - Slepian2, 5 - Slepian3\n", "\n", "Variable Name: FFTInRng\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: uint16\n", " Name: ADC input voltage range (+/-)\n", " Units: mV\n", "\n", "Variable Name: SWVersion\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: uint16\n", " Name: Software version * 100. For old software versions the value is 0.\n", "\n", "Variable Name: NoiseFilt\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Noise filter threshold factor (multiple of power noise density STD)\n", "\n", "Variable Name: CalibrOffset\n", "Dimensions: ()\n", "Shape: ()\n", "Data Type: float32\n", " Name: Calibration offset applied to the data\n", " Units: dB\n", "\n", "Variable Name: Time\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: uint32\n", " Name: Time\n", " Units: Number of seconds since 1/1/2001 00:00:00 [UTC]\n", "\n", "Variable Name: Timems\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: int32\n", " Name: Milliseconds\n", " Units: ms\n", "\n", "Variable Name: QualFlag\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: |S1\n", " Name: Quality flag. Bit 1: ADC saturation, Bit 2: spectral width too high, Bit 3: no transmitter power leveling\n", "\n", "Variable Name: Rain\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Rain rate from weather station\n", " Units: mm/h\n", "\n", "Variable Name: SurfRelHum\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Relative humidity from weather station\n", " Units: %\n", "\n", "Variable Name: SurfTemp\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Surface temperature from weather station\n", " Units: K\n", "\n", "Variable Name: SurfPres\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Surface atmospheric pressure from weather station\n", " Units: hPa\n", "\n", "Variable Name: SurfWS\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Surface wind speed from weather station\n", " Units: m/s\n", "\n", "Variable Name: SurfWD\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Surface wind direction from weather station\n", " Units: deg\n", "\n", "Variable Name: DDVolt\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Direct detection channel voltage\n", " Units: V\n", "\n", "Variable Name: DDTb\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Direct detection brightness temperature\n", " Units: K\n", "\n", "Variable Name: LWP\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Liquid water path\n", " Units: g/m^2\n", "\n", "Variable Name: PowIF\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: IF power at ADC\n", " Units: uW\n", "\n", "Variable Name: Elv\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Elevation\n", " Units: deg\n", "\n", "Variable Name: Azm\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Azimuth\n", " Units: deg\n", "\n", "Variable Name: Status\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Mitigation status flags. 0/1: heater switch (ON/OFF), 0/10: blower switch (ON/OFF)\n", "\n", "Variable Name: TPow\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Transmitted power\n", " Units: W\n", "\n", "Variable Name: TTemp\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Transmitter temperature\n", " Units: K\n", "\n", "Variable Name: RTemp\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Receiver temperature\n", " Units: K\n", "\n", "Variable Name: PCTemp\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: PC temperature\n", " Units: K\n", "\n", "Variable Name: Res_1\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Active and passive channel noise power ratio\n", "\n", "Variable Name: Res_2\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Reserved float\n", "\n", "Variable Name: Res_3\n", "Dimensions: ('Time',)\n", "Shape: (1220,)\n", "Data Type: float32\n", " Name: Reserved float\n", "\n", "Variable Name: TProf\n", "Dimensions: ('Time', 'TAlt')\n", "Shape: (1220, 93)\n", "Data Type: float32\n", " Name: Temperature profiles from RPG radiometer\n", " Units: K\n", "\n", "Variable Name: AHProf\n", "Dimensions: ('Time', 'HAlt')\n", "Shape: (1220, 93)\n", "Data Type: float32\n", " Name: Absolute humidity profiles from RPG radiometer\n", " Units: g/m^3\n", "\n", "Variable Name: RHProf\n", "Dimensions: ('Time', 'HAlt')\n", "Shape: (1220, 93)\n", "Data Type: float32\n", " Name: Relative humidity profiles from RPG radiometer\n", " Units: %\n", "\n", "Variable Name: C1PNv\n", "Dimensions: ('Time', 'C1Range')\n", "Shape: (1220, 20)\n", "Data Type: float32\n", " Name: Total IF power at vertical polarization measured at ADC input: Chirp 1\n", " Units: ???\n", "\n", "Variable Name: C1SLv\n", "Dimensions: ('Time', 'C1Range')\n", "Shape: (1220, 20)\n", "Data Type: float32\n", " Name: Sensitivity limit for vertical polarization: Chirp 1\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C1PNh\n", "Dimensions: ('Time', 'C1Range')\n", "Shape: (1220, 20)\n", "Data Type: float32\n", " Name: Total IF power at horizontal polarization measured at ADC input: Chirp 1\n", " Units: ???\n", "\n", "Variable Name: C1SLh\n", "Dimensions: ('Time', 'C1Range')\n", "Shape: (1220, 20)\n", "Data Type: float32\n", " Name: Sensitivity limit for horizontal polarization: Chirp 1\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C2PNv\n", "Dimensions: ('Time', 'C2Range')\n", "Shape: (1220, 53)\n", "Data Type: float32\n", " Name: Total IF power at vertical polarization measured at ADC input: Chirp 2\n", " Units: ???\n", "\n", "Variable Name: C2SLv\n", "Dimensions: ('Time', 'C2Range')\n", "Shape: (1220, 53)\n", "Data Type: float32\n", " Name: Sensitivity limit for vertical polarization: Chirp 2\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C2PNh\n", "Dimensions: ('Time', 'C2Range')\n", "Shape: (1220, 53)\n", "Data Type: float32\n", " Name: Total IF power at horizontal polarization measured at ADC input: Chirp 2\n", " Units: ???\n", "\n", "Variable Name: C2SLh\n", "Dimensions: ('Time', 'C2Range')\n", "Shape: (1220, 53)\n", "Data Type: float32\n", " Name: Sensitivity limit for horizontal polarization: Chirp 2\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C3PNv\n", "Dimensions: ('Time', 'C3Range')\n", "Shape: (1220, 265)\n", "Data Type: float32\n", " Name: Total IF power at vertical polarization measured at ADC input: Chirp 3\n", " Units: ???\n", "\n", "Variable Name: C3SLv\n", "Dimensions: ('Time', 'C3Range')\n", "Shape: (1220, 265)\n", "Data Type: float32\n", " Name: Sensitivity limit for vertical polarization: Chirp 3\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C3PNh\n", "Dimensions: ('Time', 'C3Range')\n", "Shape: (1220, 265)\n", "Data Type: float32\n", " Name: Total IF power at horizontal polarization measured at ADC input: Chirp 3\n", " Units: ???\n", "\n", "Variable Name: C3SLh\n", "Dimensions: ('Time', 'C3Range')\n", "Shape: (1220, 265)\n", "Data Type: float32\n", " Name: Sensitivity limit for horizontal polarization: Chirp 3\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C1Fr\n", "Dimensions: ('C1Range',)\n", "Shape: (20,)\n", "Data Type: int32\n", " Name: Range factor\n", "\n", "Variable Name: C1Range\n", "Dimensions: ('C1Range',)\n", "Shape: (20,)\n", "Data Type: float32\n", " Name: Range\n", "\n", "Variable Name: C2Fr\n", "Dimensions: ('C2Range',)\n", "Shape: (53,)\n", "Data Type: int32\n", " Name: Range factor\n", "\n", "Variable Name: C2Range\n", "Dimensions: ('C2Range',)\n", "Shape: (53,)\n", "Data Type: float32\n", " Name: Range\n", "\n", "Variable Name: C3Fr\n", "Dimensions: ('C3Range',)\n", "Shape: (265,)\n", "Data Type: int32\n", " Name: Range factor\n", "\n", "Variable Name: C3Range\n", "Dimensions: ('C3Range',)\n", "Shape: (265,)\n", "Data Type: float32\n", " Name: Range\n", "\n", "Variable Name: C1VNoisePow\n", "Dimensions: ('Time', 'C1Range')\n", "Shape: (1220, 20)\n", "Data Type: float32\n", " Name: Integrated Doppler spectrum noise pover in vertical channel: Chirp 1\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C1HNoisePow\n", "Dimensions: ('Time', 'C1Range')\n", "Shape: (1220, 20)\n", "Data Type: float32\n", " Name: Integrated Doppler spectrum noise pover in horizontal channel: Chirp 1\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C2VNoisePow\n", "Dimensions: ('Time', 'C2Range')\n", "Shape: (1220, 53)\n", "Data Type: float32\n", " Name: Integrated Doppler spectrum noise pover in vertical channel: Chirp 2\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C2HNoisePow\n", "Dimensions: ('Time', 'C2Range')\n", "Shape: (1220, 53)\n", "Data Type: float32\n", " Name: Integrated Doppler spectrum noise pover in horizontal channel: Chirp 2\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C3VNoisePow\n", "Dimensions: ('Time', 'C3Range')\n", "Shape: (1220, 265)\n", "Data Type: float32\n", " Name: Integrated Doppler spectrum noise pover in vertical channel: Chirp 3\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C3HNoisePow\n", "Dimensions: ('Time', 'C3Range')\n", "Shape: (1220, 265)\n", "Data Type: float32\n", " Name: Integrated Doppler spectrum noise pover in horizontal channel: Chirp 3\n", " Units: mm^6/m^3\n", "\n", "Variable Name: C1VSpec\n", "Dimensions: ('Time', 'C1Range', 'C1Vel')\n", "Shape: (1220, 20, 512)\n", "Data Type: float32\n", " Name: Doppler spectrum at vertical polarization: Chirp 1\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C1HSpec\n", "Dimensions: ('Time', 'C1Range', 'C1Vel')\n", "Shape: (1220, 20, 512)\n", "Data Type: float32\n", " Name: Doppler spectrum at horizontal polarization: Chirp 1\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C1ReVHSpec\n", "Dimensions: ('Time', 'C1Range', 'C1Vel')\n", "Shape: (1220, 20, 512)\n", "Data Type: float32\n", " Name: Real part of covariance spectrum: Chirp 1\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C1ImVHSpec\n", "Dimensions: ('Time', 'C1Range', 'C1Vel')\n", "Shape: (1220, 20, 512)\n", "Data Type: float32\n", " Name: Imaginary part of covariance spectrum: Chirp 1\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C2VSpec\n", "Dimensions: ('Time', 'C2Range', 'C2Vel')\n", "Shape: (1220, 53, 512)\n", "Data Type: float32\n", " Name: Doppler spectrum at vertical polarization: Chirp 2\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C2HSpec\n", "Dimensions: ('Time', 'C2Range', 'C2Vel')\n", "Shape: (1220, 53, 512)\n", "Data Type: float32\n", " Name: Doppler spectrum at horizontal polarization: Chirp 2\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C2ReVHSpec\n", "Dimensions: ('Time', 'C2Range', 'C2Vel')\n", "Shape: (1220, 53, 512)\n", "Data Type: float32\n", " Name: Real part of covariance spectrum: Chirp 2\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C2ImVHSpec\n", "Dimensions: ('Time', 'C2Range', 'C2Vel')\n", "Shape: (1220, 53, 512)\n", "Data Type: float32\n", " Name: Imaginary part of covariance spectrum: Chirp 2\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C3VSpec\n", "Dimensions: ('Time', 'C3Range', 'C3Vel')\n", "Shape: (1220, 265, 256)\n", "Data Type: float32\n", " Name: Doppler spectrum at vertical polarization: Chirp 3\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C3HSpec\n", "Dimensions: ('Time', 'C3Range', 'C3Vel')\n", "Shape: (1220, 265, 256)\n", "Data Type: float32\n", " Name: Doppler spectrum at horizontal polarization: Chirp 3\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C3ReVHSpec\n", "Dimensions: ('Time', 'C3Range', 'C3Vel')\n", "Shape: (1220, 265, 256)\n", "Data Type: float32\n", " Name: Real part of covariance spectrum: Chirp 3\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n", "\n", "Variable Name: C3ImVHSpec\n", "Dimensions: ('Time', 'C3Range', 'C3Vel')\n", "Shape: (1220, 265, 256)\n", "Data Type: float32\n", " Name: Imaginary part of covariance spectrum: Chirp 3\n", " Units: mm^6/m^3\n", " _FillValue: -999.0\n" ] } ], "source": [ "# Print dataset information\n", "print(\"NetCDF Dataset Information:\")\n", "print(dataset)\n", "\n", "# Print global attributes\n", "print(\"\\nGlobal Attributes:\")\n", "for attr_name in dataset.ncattrs():\n", " print(f\"{attr_name}: {getattr(dataset, attr_name)}\")\n", "\n", "# Print dimensions\n", "print(\"\\nDimensions:\")\n", "for dim_name, dim in dataset.dimensions.items():\n", " print(f\"{dim_name}: {len(dim)}\")\n", "\n", "# Print variables and their attributes\n", "print(\"\\nVariables:\")\n", "for var_name, var in dataset.variables.items():\n", " print(f\"\\nVariable Name: {var_name}\")\n", " print(f\"Dimensions: {var.dimensions}\")\n", " print(f\"Shape: {var.shape}\")\n", " print(f\"Data Type: {var.dtype}\")\n", " for attr_name in var.ncattrs():\n", " print(f\" {attr_name}: {getattr(var, attr_name)}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "b321e05d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "id": "ea7c4f21", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Path to the folder containing the data files\n", "#folder_path = 'C:\\\\Users\\\\magda\\\\Master Thesis\\\\Cloud Radar\\\\2024-05\\\\2024-05-03' # Replace \"your_folder_path\" with the path to your folder\n", "#file_list = [f for f in os.listdir(folder_path) if f.endswith(\"LV1.nc\")]\n", "\n", "#print(\"Files found in the folder:\")\n", "#print(len(file_list))" ] }, { "cell_type": "code", "execution_count": 7, "id": "75c006f9", "metadata": { "scrolled": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'file_list' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[7], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Loop through each file\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m file_name \u001b[38;5;129;01min\u001b[39;00m file_list:\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Open the NetCDF file\u001b[39;00m\n\u001b[0;32m 4\u001b[0m file_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, file_name)\n\u001b[0;32m 5\u001b[0m dataset \u001b[38;5;241m=\u001b[39m Dataset(file_path)\n", "\u001b[1;31mNameError\u001b[0m: name 'file_list' is not defined" ] } ], "source": [ "\n", "# Loop through each file\n", "for file_name in file_list:\n", " # Open the NetCDF file\n", " file_path = os.path.join(folder_path, file_name)\n", " dataset = Dataset(file_path)\n", "\n", " # Read necessary variables\n", " surf_temp_data = dataset.variables['SurfTemp'][:]\n", " rh_data = dataset.variables['SurfRelHum'][:]\n", " surf_ws_data = dataset.variables['SurfWS'][:]\n", " lwp_data = dataset.variables['LWP'][:]\n", " surf_pres_data = dataset.variables['SurfPres'][:]\n", " rain_rate_data = dataset.variables['Rain'][:]\n", " \n", " # Get time data\n", " time_data = dataset.variables['Time'][:]\n", " timems_data = dataset.variables['Timems'][:]\n", " start_time = datetime(2001, 1, 1, 0, 0, 0)\n", " time = [start_time + timedelta(seconds=int(time_data[i]), milliseconds=int(timems_data[i])) for i in range(len(time_data))]\n", " \n", " # Plot variables\n", " plt.figure(figsize=(12, 8))\n", " \n", " plt.subplot(3, 2, 1)\n", " plt.plot(time, surf_temp_data, color='b')\n", " plt.title('Surface Temperature')\n", " plt.xlabel('Time')\n", " plt.ylabel('Temperature (K)')\n", " \n", " plt.subplot(3, 2, 2)\n", " plt.plot(time, rh_data, color='r')\n", " plt.title('Relative Humidity')\n", " plt.xlabel('Time')\n", " plt.ylabel('Relative Humidity (%)')\n", " \n", " plt.subplot(3, 2, 3)\n", " plt.plot(time, surf_ws_data, color='g')\n", " plt.title('Surface Wind Speed')\n", " plt.xlabel('Time')\n", " plt.ylabel('Wind Speed (m/s)')\n", " \n", " plt.subplot(3, 2, 4)\n", " plt.plot(time, lwp_data, color='m')\n", " plt.title('Liquid Water Path')\n", " plt.xlabel('Time')\n", " plt.ylabel('LWP (g/m^2)')\n", " \n", " plt.subplot(3, 2, 5)\n", " plt.plot(time, surf_pres_data, color='c')\n", " plt.title('Surface Pressure')\n", " plt.xlabel('Time')\n", " plt.ylabel('Pressure (hPa)')\n", " \n", " plt.subplot(3, 2, 6)\n", " plt.plot(time, rain_rate_data, color='y')\n", " plt.title('Rain Rate')\n", " plt.xlabel('Time')\n", " plt.ylabel('Rain Rate (mm/hr)')\n", " \n", " plt.tight_layout()\n", " \n", " # Save plot\n", " plot_name = os.path.splitext(file_name)[0] + '_plot.png'\n", " plt.savefig(os.path.join(folder_path, plot_name))\n", " plt.close()\n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "a07089d6", "metadata": {}, "outputs": [], "source": [ "print(dataset['TAlts'][:])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf535169", "metadata": {}, "outputs": [], "source": [ "# Initialize lists to store data\n", "all_surf_temp_data = []\n", "all_rh_data = []\n", "all_surf_ws_data = []\n", "all_lwp_data = []\n", "all_surf_pres_data = []\n", "all_rain_rate_data = []\n", "all_time = []\n", "# Initialize lists to store data for profiles\n", "all_TProf_data = []\n", "all_AHProf_data = []\n", "all_RHProf_data = []\n", "all_profile_time_utc = []\n", "\n", "# Loop through each file\n", "for file_name in file_list:\n", " # Open the NetCDF file\n", " file_path = os.path.join(folder_path, file_name)\n", " dataset = Dataset(file_path)\n", "\n", " # Read necessary variables\n", " surf_temp_data = dataset.variables['SurfTemp'][:]\n", " rh_data = dataset.variables['SurfRelHum'][:]\n", " surf_ws_data = dataset.variables['SurfWS'][:]\n", " lwp_data = dataset.variables['LWP'][:]\n", " surf_pres_data = dataset.variables['SurfPres'][:]\n", " rain_rate_data = dataset.variables['Rain'][:]\n", "\n", " # Get time data\n", " time_data = dataset.variables['Time'][:]\n", " timems_data = dataset.variables['Timems'][:]\n", " start_time = datetime(2001, 1, 1, 0, 0, 0)\n", " time = [start_time + timedelta(seconds=int(time_data[i]), milliseconds=int(timems_data[i])) for i in range(len(time_data))]\n", "\n", " # Append data to lists\n", " all_surf_temp_data.extend(surf_temp_data)\n", " all_rh_data.extend(rh_data)\n", " all_surf_ws_data.extend(surf_ws_data)\n", " all_lwp_data.extend(lwp_data)\n", " all_surf_pres_data.extend(surf_pres_data)\n", " all_rain_rate_data.extend(rain_rate_data)\n", " all_time.extend(time)\n", " #Read profile variables\n", " # Read profile variables\n", " TProf_data = dataset.variables['TProf'][:]\n", " AHProf_data = dataset.variables['AHProf'][:]\n", " RHProf_data = dataset.variables['RHProf'][:]\n", " profile_time_data = dataset.variables['Time'][:]\n", "\n", " # Convert profile time data to readable UTC format\n", " profile_start_time = datetime(2001, 1, 1, 0, 0, 0)\n", " profile_time_utc = [profile_start_time + timedelta(seconds=int(t)) for t in profile_time_data]\n", " # Append profile data to lists\n", " all_TProf_data.append(TProf_data)\n", " all_profile_time_utc.extend(profile_time_utc)\n", " all_AHProf_data.append(AHProf_data)\n", " all_RHProf_data.append(RHProf_data)\n", " # Get altitude data\n", " TAlt_data = dataset.variables['TAlts'][:]\n", " HAlt_data = dataset.variables['HAlts'][:]\n", "\n", " # Plot the profiles\n", " fig, axs = plt.subplots(3, 1, figsize=(15, 15))\n", "\n", " # Temperature Profile\n", " cax1 = axs[0].imshow(TProf_data.T, extent=[np.min(profile_time_utc), np.max(profile_time_utc), np.min(TAlt_data), np.max(TAlt_data)], aspect='auto', origin='lower')\n", " axs[0].set_title('Temperature Profile')\n", " axs[0].set_xlabel('Time (UTC)')\n", " axs[0].set_ylabel('Altitude (m)')\n", " plt.colorbar(cax1, ax=axs[0], label='Temperature (K)')\n", "\n", " # Absolute Humidity Profile\n", " cax2 = axs[1].imshow(AHProf_data.T, extent=[np.min(profile_time_utc), np.max(profile_time_utc), np.min(HAlt_data), np.max(HAlt_data)], aspect='auto', origin='lower')\n", " axs[1].set_title('Absolute Humidity Profile')\n", " axs[1].set_xlabel('Time (UTC)')\n", " axs[1].set_ylabel('Altitude (m)')\n", " plt.colorbar(cax2, ax=axs[1], label='Absolute Humidity (g/m^3)')\n", "\n", " # Relative Humidity Profile\n", " cax3 = axs[2].imshow(RHProf_data.T, extent=[np.min(profile_time_utc), np.max(profile_time_utc), np.min(HAlt_data), np.max(HAlt_data)], aspect='auto', origin='lower')\n", " axs[2].set_title('Relative Humidity Profile')\n", " axs[2].set_xlabel('Time (UTC)')\n", " axs[2].set_ylabel('Altitude (m)')\n", " plt.colorbar(cax3, ax=axs[2], label='Relative Humidity (%)')\n", "\n", " # Adjust layout\n", " plt.tight_layout()\n", " # Save plot\n", " profile_plot_name = os.path.splitext(file_name)[0] + '_profile_plot.png'\n", " plt.savefig(os.path.join(folder_path, profile_plot_name))\n", " plt.close()\n", "\n", "print(\"Plots created for all files.\")" ] }, { "cell_type": "code", "execution_count": null, "id": "51abb34c", "metadata": { "scrolled": true }, "outputs": [], "source": [ "\n", "# Plot variables for the entire day\n", "plt.figure(figsize=(12, 8))\n", "\n", "plt.subplot(3, 2, 1)\n", "plt.plot(all_time, all_surf_temp_data, color='b')\n", "plt.title('Surface Temperature')\n", "plt.xlabel('Time')\n", "plt.ylabel('Temperature (K)')\n", "plt.xticks(rotation=45)\n", "\n", "plt.subplot(3, 2, 2)\n", "plt.plot(all_time, all_rh_data, color='r')\n", "plt.title('Relative Humidity')\n", "plt.xlabel('Time')\n", "plt.ylabel('Relative Humidity (%)')\n", "plt.xticks(rotation=45)\n", "\n", "plt.subplot(3, 2, 3)\n", "plt.plot(all_time, all_surf_ws_data, color='g')\n", "plt.title('Surface Wind Speed')\n", "plt.xlabel('Time')\n", "plt.ylabel('Wind Speed (m/s)')\n", "plt.xticks(rotation=45)\n", "\n", "plt.subplot(3, 2, 4)\n", "plt.plot(all_time, all_lwp_data, color='m')\n", "plt.title('Liquid Water Path')\n", "plt.xlabel('Time')\n", "plt.ylabel('LWP (g/m^2)')\n", "plt.xticks(rotation=45)\n", "\n", "plt.subplot(3, 2, 5)\n", "plt.plot(all_time, all_surf_pres_data, color='c')\n", "plt.title('Surface Pressure')\n", "plt.xlabel('Time')\n", "plt.ylabel('Pressure (hPa)')\n", "plt.xticks(rotation=45)\n", "\n", "plt.subplot(3, 2, 6)\n", "plt.plot(all_time, all_rain_rate_data, color='y')\n", "plt.title('Rain Rate')\n", "plt.xlabel('Time')\n", "plt.ylabel('Rain Rate (mm/hr)')\n", "plt.xticks(rotation=45)\n", "\n", "plt.tight_layout()\n", "\n", "# Save plot\n", "plot_name = 'daily_plots.png'\n", "plt.savefig(os.path.join(folder_path, plot_name))" ] }, { "cell_type": "code", "execution_count": null, "id": "369ad25d", "metadata": {}, "outputs": [], "source": [ "# Combine all profile data for the whole day\n", "all_TProf_data = np.concatenate(all_TProf_data, axis=0)\n", "all_AHProf_data = np.concatenate(all_AHProf_data, axis=0)\n", "all_RHProf_data = np.concatenate(all_RHProf_data, axis=0)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "37069fa7", "metadata": {}, "outputs": [], "source": [ "\n", "# Plot combined profiles for the whole day\n", "fig, axs = plt.subplots(3, 1, figsize=(15, 15))\n", "\n", "# Combined Temperature Profile\n", "cax1 = axs[0].imshow(all_TProf_data.T, extent=[np.min(all_profile_time_utc), np.max(all_profile_time_utc), np.min(TAlt_data), np.max(TAlt_data)], aspect='auto', origin='lower')\n", "axs[0].set_title('Combined Temperature Profile for the Whole Day')\n", "axs[0].set_xlabel('Time (UTC)')\n", "axs[0].set_ylabel('Altitude (m)')\n", "plt.colorbar(cax1, ax=axs[0], label='Temperature (K)')\n", "\n", "# Combined Absolute Humidity Profile\n", "cax2 = axs[1].imshow(all_AHProf_data.T, extent=[np.min(all_profile_time_utc), np.max(all_profile_time_utc), np.min(HAlt_data), np.max(HAlt_data)], aspect='auto', origin='lower')\n", "axs[1].set_title('Combined Absolute Humidity Profile for the Whole Day')\n", "axs[1].set_xlabel('Time (UTC)')\n", "axs[1].set_ylabel('Altitude (m)')\n", "plt.colorbar(cax2, ax=axs[1], label='Absolute Humidity (g/m^3)')\n", "\n", "# Combined Relative Humidity Profile\n", "cax3 = axs[2].imshow(all_RHProf_data.T, extent=[np.min(all_profile_time_utc), np.max(all_profile_time_utc), np.min(HAlt_data), np.max(HAlt_data)], aspect='auto', origin='lower')\n", "axs[2].set_title('Combined Relative Humidity Profile for the Whole Day')\n", "axs[2].set_xlabel('Time (UTC)')\n", "axs[2].set_ylabel('Altitude (m)')\n", "plt.colorbar(cax3, ax=axs[2], label='Relative Humidity (%)')\n", "\n", "# Adjust layout\n", "plt.tight_layout()\n", "# Save combined profile plot\n", "combined_profile_plot_name = 'combined_daily_profile_plot.png'\n", "plt.savefig(os.path.join(folder_path, combined_profile_plot_name))\n", "plt.close()" ] }, { "cell_type": "code", "execution_count": null, "id": "38534c93", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Print time data in readable form\n", "#for t in all_time:\n", " #print(t.strftime('%Y-%m-%d %H:%M:%S'))\n" ] }, { "cell_type": "markdown", "id": "212ac60f", "metadata": {}, "source": [ "### Explore cloud base" ] }, { "cell_type": "code", "execution_count": null, "id": "50680736", "metadata": {}, "outputs": [], "source": [ "'''\n", "# Base directory containing monthly folders\n", "base_dir_cr = 'C:\\\\Users\\\\magda\\\\Master_Thesis\\\\Cloud_radar'\n", "month = '2024-05' # The folder for May\n", "day = '2024-05-02' # The specific date folder\n", "day_folder_path = os.path.join(base_dir_cr, month, day)\n", "\n", "\n", "# Construct the directory path for the specific date\n", "day_folder_path = os.path.join(base_dir_cr, month, day)\n", "\n", "# Print the constructed path for debugging\n", "print(f\"Constructed day folder path: {day_folder_path}\")\n", "\n", "# Define the specific file name you want to focus on\n", "file_name = '240503_000002_P00_ZEN.LV0.nc' # Replace with the actual file name if known\n", "file_path = os.path.join(day_folder_path, file_name)\n", "\n", "# Create a PDF file\n", "pdf_path = 'LV0_NC_Variables_Report.pdf'\n", "doc = SimpleDocTemplate(pdf_path, pagesize=letter)\n", "story = []\n", "\n", "# Define styles\n", "styles = getSampleStyleSheet()\n", "title_style = styles['Title']\n", "normal_style = styles['Normal']\n", "\n", "# Check if the file exists\n", "if not os.path.exists(file_path):\n", " print(f\"File {file_path} does not exist.\")\n", "else:\n", " print(f\"File {file_path} exists. Generating report...\")\n", "\n", " # Open the NetCDF file\n", " dataset = Dataset(file_path, 'r')\n", "\n", " # Add file name as title\n", " story.append(Paragraph(f'File: {file_name}', title_style))\n", " story.append(Spacer(1, 12))\n", "\n", " # Add variable information\n", " story.append(Paragraph('Variables and Attributes:', title_style))\n", " story.append(Spacer(1, 12))\n", " \n", " for var_name, var in dataset.variables.items():\n", " var_info = f\"Variable Name: {var_name}\\n\"\n", " var_info += f\" Dimensions: {var.dimensions}\\n\"\n", " var_info += f\" Shape: {var.shape}\\n\"\n", " var_info += f\" Data Type: {var.dtype}\\n\"\n", " \n", " # Add attributes\n", " for attr_name in var.ncattrs():\n", " var_info += f\" {attr_name}: {getattr(var, attr_name)}\\n\"\n", " \n", " var_info += \"\\n\"\n", " \n", " story.append(Paragraph(var_info, normal_style))\n", "\n", " # Close the dataset\n", " dataset.close()\n", "\n", " # Build the PDF\n", " doc.build(story)\n", "\n", " # print(f\"PDF saved to {pdf_path}\")\n", "'''" ] }, { "cell_type": "code", "execution_count": null, "id": "051161f5", "metadata": { "scrolled": true }, "outputs": [], "source": [ "'''\n", "# Initialize a list to store the CBH data\n", "cbh_data = []\n", "\n", "# Check if the directory exists\n", "if not os.path.exists(day_folder_path):\n", " print(f\"Directory {day_folder_path} does not exist.\")\n", "else:\n", " print(f\"Directory {day_folder_path} exists. Searching for LV1.nc files...\")\n", "\n", " # List all files in the specific date folder for debugging\n", " all_files = os.listdir(day_folder_path)\n", " print(f\"All files in {day_folder_path}: {all_files}\")\n", "\n", " # Search for LV1.nc files in the specific date folder\n", " for file_name in all_files:\n", " if file_name.lower().endswith('lv1.nc'):\n", " file_path = os.path.join(day_folder_path, file_name)\n", " print(f\"Found file: {file_path}\") # Debug: Print the file path\n", " \n", " # Open the NetCDF file\n", " dataset = Dataset(file_path, 'r')\n", " \n", " # Extract the CBH variable and the time variable\n", " cbh = dataset.variables['CBH'][:]\n", " time_data = dataset.variables['Time'][:]\n", " timems_data = dataset.variables['Timems'][:]\n", " start_time = datetime(2001, 1, 1, 0, 0, 0)\n", " time = [start_time + timedelta(seconds=int(time_data[i]), milliseconds=int(timems_data[i])) for i in range(len(time_data))]\n", " \n", " # Create a DataFrame for this file's data\n", " df = pd.DataFrame({'Timestamp': time, 'CBH': cbh})\n", " \n", " # Append the DataFrame to the list\n", " cbh_data.append(df)\n", " \n", " # Close the dataset\n", " dataset.close()\n", "\n", "# Combine all DataFrames into a single DataFrame if any data was collected\n", "if cbh_data:\n", " df_cbh_combined = pd.concat(cbh_data, ignore_index=True)\n", " \n", " # Print the combined DataFrame\n", " print(df_cbh_combined)\n", " \n", " # Optionally, save the combined DataFrame to a CSV or Parquet file\n", " #output_csv_path = os.path.join(base_dir_cr, 'CBH_Data_2024-05-03.csv')\n", " #df_cbh_combined.to_csv(output_csv_path, index=False)\n", " #print(f\"Combined data saved to {output_csv_path}\")\n", "else:\n", " print(\"No LV1.nc files were found in the directory.\")\n", "'''" ] }, { "cell_type": "markdown", "id": "c16ad0d2", "metadata": {}, "source": [ "### Load Rain " ] }, { "cell_type": "code", "execution_count": 20, "id": "b32f3d85", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Directory C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23 exists. Searching for LV1.nc files...\n", "All files in C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23: ['240523_000002_P00_ZEN.LV1.nc', '240523_010000_P00_ZEN.LV1.nc', '240523_020000_P00_ZEN.LV1.nc', '240523_030001_P00_ZEN.LV1.nc', '240523_040000_P00_ZEN.LV1.nc', '240523_050000_P00_ZEN.LV1.nc', '240523_060001_P00_ZEN.LV1.nc', '240523_070001_P00_ZEN.LV1.nc', '240523_080000_P00_ZEN.LV1.nc', '240523_090001_P00_ZEN.LV1.nc', '240523_100000_P00_ZEN.LV1.nc', '240523_110000_P00_ZEN.LV1.nc', '240523_120001_P00_ZEN.LV1.nc', '240523_130000_P00_ZEN.LV1.nc', '240523_140001_P00_ZEN.LV1.nc', '240523_150000_P00_ZEN.LV1.nc', '240523_160000_P00_ZEN.LV1.nc', '240523_170000_P00_ZEN.LV1.nc', '240523_180000_P00_ZEN.LV1.nc', '240523_190000_P00_ZEN.LV1.nc', '240523_200000_P00_ZEN.LV1.nc', '240523_210000_P00_ZEN.LV1.nc', '240523_220001_P00_ZEN.LV1.nc', '240523_230000_P00_ZEN.LV1.nc', 'cloud_radar_vertical_dataset_10min.parquet', 'Rain_10min_Averages.csv']\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_000002_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_010000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_020000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_030001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_040000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_050000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_060001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_070001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_080000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_090001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_100000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_110000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_120001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_130000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_140001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_150000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_160000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_170000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_180000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_190000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_200000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_210000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_220001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-23\\240523_230000_P00_ZEN.LV1.nc\n", " TIMESTAMP Rain\n", "0 2024-05-23 00:00:02.332 0.0\n", "1 2024-05-23 00:00:05.252 0.0\n", "2 2024-05-23 00:00:08.172 0.0\n", "3 2024-05-23 00:00:11.092 0.0\n", "4 2024-05-23 00:00:14.012 0.0\n", "... ... ...\n", "29305 2024-05-23 23:59:45.867 0.0\n", "29306 2024-05-23 23:59:48.787 0.0\n", "29307 2024-05-23 23:59:51.707 0.0\n", "29308 2024-05-23 23:59:54.627 0.0\n", "29309 2024-05-23 23:59:57.537 0.0\n", "\n", "[29310 rows x 2 columns]\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Base directory containing monthly folders\n", "base_dir_cr = 'C:\\\\Users\\\\magda\\\\Master_Thesis\\\\Cloud_radar'\n", "month = '2024-05' #e folder for May\n", "day = '2024-05-23' \n", "\n", "# Construct the directory path for the specific date\n", "day_folder_path = os.path.join(base_dir_cr, month, day)\n", "\n", "# Initialize a list to store the Rain data\n", "rain_data_list = []\n", "\n", "# Check if the directory exists\n", "if not os.path.exists(day_folder_path):\n", " print(f\"Directory {day_folder_path} does not exist.\")\n", "else:\n", " print(f\"Directory {day_folder_path} exists. Searching for LV1.nc files...\")\n", "\n", " # List all files in the specific date folder for debugging\n", " all_files = os.listdir(day_folder_path)\n", " print(f\"All files in {day_folder_path}: {all_files}\")\n", "\n", " # Search for LV1.nc files in the specific date folder\n", " for file_name in all_files:\n", " if file_name.lower().endswith('lv1.nc'):\n", " file_path = os.path.join(day_folder_path, file_name)\n", " print(f\"Found file: {file_path}\") # Debug: Print the file path\n", " \n", " # Open the NetCDF file\n", " dataset = Dataset(file_path, 'r')\n", " \n", " # Extract the Rain variable and the time variable\n", " rain_data = dataset.variables['Rain'][:]\n", " time_data = dataset.variables['Time'][:]\n", " timems_data = dataset.variables['Timems'][:]\n", " start_time = datetime(2001, 1, 1, 0, 0, 0)\n", " time = [start_time + timedelta(seconds=int(time_data[i]), milliseconds=int(timems_data[i])) for i in range(len(time_data))]\n", " \n", " # Create a DataFrame for this file's data\n", " df = pd.DataFrame({'TIMESTAMP': time, 'Rain': rain_data})\n", " \n", " # Append the DataFrame to the list\n", " rain_data_list.append(df)\n", " \n", " # Close the dataset\n", " dataset.close()\n", "\n", "## Combine all DataFrames into a single DataFrame if any data was collected\n", "if rain_data_list:\n", " df_rain_combined = pd.concat(rain_data_list, ignore_index=True)\n", "\n", " # Print the combined DataFrame (optional)\n", " print(df_rain_combined)\n", "\n", " # Plot: Rain Rate vs Time (clean & presentation-ready)\n", " # plt.figure(figsize=(8,6))\n", " #plt.plot(df_rain_combined['TIMESTAMP'], df_rain_combined['Rain'], \n", " # label='Rain Rate', linewidth=2, color='teal')\n", "\n", " # Axis labels and title\n", " #plt.xlabel('Time', fontsize=20)\n", " #plt.ylabel('Rain Rate (mm/h)', fontsize=20)\n", " #plt.title('Rain Rate vs Time', fontsize=20, weight='bold')\n", "\n", " # Ticks and grid\n", " #plt.xticks(fontsize=18, rotation=45)\n", " #plt.yticks(fontsize=18)\n", " #plt.grid(True, linestyle='--', alpha=0.7)\n", "\n", " # Create a larger figure\n", " fig, ax = plt.subplots(figsize=(8, 6))\n", " # 2) Thicken all spines (axis borders)\n", " for spine in ax.spines.values():\n", " spine.set_linewidth(1.5)\n", " # 1) Plot LWP_Corrected with a bolder line\n", " ax.plot(df_rain_combined['TIMESTAMP'], df_rain_combined['Rain'], \n", " label='Rain Rate', linewidth=2, color='blue')\n", " # 3) Tick parameters for major & minor ticks\n", " ax.tick_params(axis='both', which='major', labelsize=12, width=1.5, length=6)\n", " ax.tick_params(axis='both', which='minor', width=1.0, length=4)\n", "\n", " # 4) Format x‐axis to show time in HH:MM\n", " date_format = mdates.DateFormatter('%H:%M')\n", " ax.xaxis.set_major_formatter(date_format)\n", " ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))\n", " ax.xaxis.set_minor_locator(mdates.MinuteLocator(interval=30))\n", "\n", " # 5) Labels and title with bold font\n", " ax.set_title('Rain Rate vs Time', fontsize=18, fontweight='bold')\n", " ax.set_xlabel('Time', fontsize=16, fontweight='bold')\n", " ax.set_ylabel('Rain Rate (mm/h)', fontsize=16, fontweight='bold')\n", "\n", " # 6) Y‐axis tick font size\n", " ax.tick_params(axis='y', labelsize=12)\n", "\n", " # 7) Grid styling\n", " ax.grid(True, which='major', linestyle='--', linewidth=0.8, alpha=0.7)\n", " ax.grid(True, which='minor', linestyle=':', linewidth=0.5, alpha=0.5)\n", "\n", " # 8) Rotate x‐tick labels\n", " plt.xticks(rotation=45)\n", "\n", " # 9) (Optional) Legend inside plot\n", " legend = ax.legend(fontsize=14, frameon=True)\n", " legend.get_frame().set_linewidth(1.5)\n", "\n", " # 10) Tight layout and save\n", " plt.tight_layout()\n", "\n", " # Legend and layout\n", " #plt.legend(fontsize=12)\n", " # plt.tight_layout()\n", " file_path = os.path.join(day_folder_path, 'RR_report.png')\n", " plt.savefig(file_path, dpi=300)\n", " plt.show()\n", "else:\n", " print(\"No rain data available.\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "4e84fc45", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TIMESTAMP Rain\n", "0 2024-05-02 00:00:00 0.0\n", "1 2024-05-02 00:10:00 0.0\n", "2 2024-05-02 00:20:00 0.0\n", "3 2024-05-02 00:30:00 0.0\n", "4 2024-05-02 00:40:00 0.0\n", ".. ... ...\n", "139 2024-05-02 23:10:00 0.0\n", "140 2024-05-02 23:20:00 0.0\n", "141 2024-05-02 23:30:00 0.0\n", "142 2024-05-02 23:40:00 0.0\n", "143 2024-05-02 23:50:00 0.0\n", "\n", "[144 rows x 2 columns]\n" ] } ], "source": [ "# Resample the data to 10-minute averages\n", "df_rain_combined.set_index('TIMESTAMP', inplace=True)\n", "df_rain_resampled = df_rain_combined.resample('10T').mean().reset_index()\n", " \n", " # Print the resampled DataFrame\n", "print(df_rain_resampled)" ] }, { "cell_type": "code", "execution_count": 9, "id": "de4d8c14", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10-minute averages saved to C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\Rain_10min_Averages.csv\n" ] } ], "source": [ "\n", "# Save the resampled DataFrame to a CSV file in the specific date folder\n", "output_csv_path = os.path.join(day_folder_path, 'Rain_10min_Averages.csv')\n", "df_rain_resampled.to_csv(output_csv_path, index=False)\n", "print(f\"10-minute averages saved to {output_csv_path}\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "83c1caad", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 6)) # Wider figure for better visibility\n", "\n", "# Plot with thicker line and improved font\n", "plt.plot(df_rain_resampled['TIMESTAMP'], df_rain_resampled['Rain'], \n", " label='Rain Rate', linewidth=2, color='steelblue')\n", "\n", "# Axis labels with larger font\n", "plt.xlabel('Time', fontsize=14)\n", "plt.ylabel('Rain Rate (mm/h)', fontsize=14)\n", "plt.title('10-Minute Average Rain Rate vs Time', fontsize=16, weight='bold')\n", "\n", "# Tick parameters\n", "plt.xticks(fontsize=12, rotation=45) # Rotate for time series clarity\n", "plt.yticks(fontsize=12)\n", "\n", "# Format x-axis if needed (e.g., showing HH:MM)\n", "# plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", "\n", "# Add grid, legend, and tighter layout\n", "plt.grid(True, linestyle='--', alpha=0.7)\n", "plt.legend(fontsize=12)\n", "plt.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "62c027e9", "metadata": {}, "source": [ "### Vertical Profiles" ] }, { "cell_type": "code", "execution_count": 18, "id": "e280d03b", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Directory C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02 exists. Searching for LV1.nc files...\n", "All files in C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02: ['240502_000001_P00_ZEN.LV0.nc', '240502_000001_P00_ZEN.LV1.nc', '240502_010000_P00_ZEN.LV0.nc', '240502_010000_P00_ZEN.LV1.nc', '240502_020000_P00_ZEN.LV0.nc', '240502_020000_P00_ZEN.LV1.nc', '240502_030001_P00_ZEN.LV0.nc', '240502_030001_P00_ZEN.LV1.nc', '240502_040000_P00_ZEN.LV0.nc', '240502_040000_P00_ZEN.LV1.nc', '240502_050001_P00_ZEN.LV0.nc', '240502_050001_P00_ZEN.LV1.nc', '240502_060000_P00_ZEN.LV0.nc', '240502_060000_P00_ZEN.LV1.nc', '240502_070000_P00_ZEN.LV0.nc', '240502_070000_P00_ZEN.LV1.nc', '240502_080000_P00_ZEN.LV0.nc', '240502_080000_P00_ZEN.LV1.nc', '240502_090000_P00_ZEN.LV0.nc', '240502_090000_P00_ZEN.LV1.nc', '240502_100000_P00_ZEN.LV0.nc', '240502_100000_P00_ZEN.LV1.nc', '240502_110000_P00_ZEN.LV0.nc', '240502_110000_P00_ZEN.LV1.nc', '240502_120000_P00_ZEN.LV0.nc', '240502_120000_P00_ZEN.LV1.nc', '240502_130000_P00_ZEN.LV0.nc', '240502_130000_P00_ZEN.LV1.nc', '240502_140000_P00_ZEN.LV0.nc', '240502_140000_P00_ZEN.LV1.nc', '240502_150000_P00_ZEN.LV0.nc', '240502_150000_P00_ZEN.LV1.nc', '240502_160000_P00_ZEN.LV0.nc', '240502_160000_P00_ZEN.LV1.nc', '240502_170000_P00_ZEN.LV0.nc', '240502_170000_P00_ZEN.LV1.nc', '240502_180000_P00_ZEN.LV0.nc', '240502_180000_P00_ZEN.LV1.nc', '240502_190001_P00_ZEN.LV0.nc', '240502_190001_P00_ZEN.LV1.nc', '240502_200000_P00_ZEN.LV0.nc', '240502_200000_P00_ZEN.LV1.nc', '240502_210000_P00_ZEN.LV0.nc', '240502_210000_P00_ZEN.LV1.nc', '240502_220000_P00_ZEN.LV0.nc', '240502_220000_P00_ZEN.LV1.nc', '240502_230000_P00_ZEN.LV0.nc', '240502_230000_P00_ZEN.LV1.nc', 'Rain_10min_Averages.csv']\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_000001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_010000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_020000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_030001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_040000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_050001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_060000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_070000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_080000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_090000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_100000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_110000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_120000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_130000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_140000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_150000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_160000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_170000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_180000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_190001_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_200000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_210000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_220000_P00_ZEN.LV1.nc\n", "Found file: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\240502_230000_P00_ZEN.LV1.nc\n", " timestamp temperature_altitudes \\\n", "0 2024-05-02 00:00:01.502 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "1 2024-05-02 00:00:04.422 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "2 2024-05-02 00:00:07.332 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "3 2024-05-02 00:00:10.252 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "4 2024-05-02 00:00:13.182 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "\n", " temperature_profile \\\n", "0 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "1 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "2 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "3 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "4 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "\n", " humidity_altitudes \\\n", "0 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "1 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "2 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "3 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "4 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "\n", " abs_humidity_profile \\\n", "0 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "1 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "2 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "3 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "4 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "\n", " rel_humidity_profile surf_temp surf_pres \n", "0 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "1 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "2 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "3 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "4 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n" ] } ], "source": [ "# Base directory containing monthly folders\n", "base_dir_cr = 'C:\\\\Users\\\\magda\\\\Master_Thesis\\\\Cloud_radar'\n", "month = '2024-05' # The folder for June\n", "day = '2024-05-02' # The specific date folder\n", "\n", "# Construct the directory path for the specific date\n", "day_folder_path = os.path.join(base_dir_cr, month, day)\n", "\n", "# Initialize lists to store the data for each timestamp\n", "profiles_with_time = []\n", "\n", "# Variables to hold altitude data\n", "temperature_altitudes = None\n", "humidity_altitudes = None\n", "\n", "# Check if the directory exists\n", "if not os.path.exists(day_folder_path):\n", " print(f\"Directory {day_folder_path} does not exist.\")\n", "else:\n", " print(f\"Directory {day_folder_path} exists. Searching for LV1.nc files...\")\n", "\n", " # List all files in the specific date folder for debugging\n", " all_files = os.listdir(day_folder_path)\n", " print(f\"All files in {day_folder_path}: {all_files}\")\n", "\n", " # Search for LV1.nc files in the specific date folder\n", " for file_name in all_files:\n", " if file_name.lower().endswith('lv1.nc'):\n", " file_path = os.path.join(day_folder_path, file_name)\n", " print(f\"Found file: {file_path}\") # Debug: Print the file path\n", " \n", " try:\n", " # Try to open the NetCDF file\n", " dataset = Dataset(file_path, 'r')\n", "\n", " # Extract altitude data (only once)\n", " if temperature_altitudes is None:\n", " temperature_altitudes = dataset.variables['TAlts'][:] # Altitude for temperature\n", " humidity_altitudes = dataset.variables['HAlts'][:] # Altitude for humidity\n", "\n", " # Extract profiles and surface data\n", " temperature_data = dataset.variables['TProf'][:] # Temperature profiles\n", " abs_humidity_data = dataset.variables['AHProf'][:] # Absolute humidity profiles\n", " rel_humidity_data = dataset.variables['RHProf'][:] # Relative humidity profiles\n", " surf_temp_data = dataset.variables['SurfTemp'][:] # Surface temperature\n", " surf_pres_data = dataset.variables['SurfPres'][:] # Surface pressure\n", " time_data = dataset.variables['Time'][:]\n", " timems_data = dataset.variables['Timems'][:]\n", "\n", " # Convert the time data\n", " start_time = datetime(2001, 1, 1, 0, 0, 0)\n", " times = [start_time + timedelta(seconds=int(time_data[i]), milliseconds=int(timems_data[i])) for i in range(len(time_data))]\n", "\n", " # Append the data as dictionaries for each time step\n", " for i, time in enumerate(times):\n", " profiles_with_time.append({\n", " 'timestamp': time,\n", " 'temperature_altitudes': temperature_altitudes,\n", " 'temperature_profile': temperature_data[i, :],\n", " 'humidity_altitudes': humidity_altitudes,\n", " 'abs_humidity_profile': abs_humidity_data[i, :],\n", " 'rel_humidity_profile': rel_humidity_data[i, :],\n", " 'surf_temp': surf_temp_data[i], # Surface temperature for this timestamp\n", " 'surf_pres': surf_pres_data[i] # Surface pressure for this timestamp\n", " })\n", "\n", " # Close the dataset\n", " dataset.close()\n", "\n", " except OSError as e:\n", " print(f\"Error reading file {file_path}: {e}\")\n", "\n", "# Create a DataFrame from the list of profiles\n", "df = pd.DataFrame(profiles_with_time)\n", "\n", "# Set the timestamp as the index\n", "#df['timestamp'] = pd.to_datetime(df['timestamp'])\n", "\n", "#df.set_index('timestamp', inplace=True)\n", "\n", "# Print the first few rows to verify the structure\n", "print(df.head())\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "6d42a76b", "metadata": {}, "outputs": [], "source": [ "# Apply the pressure calculation for every timestamp and add it to the DataFrame\n", "df['calculated_pressures'] = calculate_pressure_at_heights(df)\n", "\n", "# Calculate saturation vapor pressure for each temperature profile\n", "df['saturation_vapor_pressure'] = df['temperature_profile'].apply(\n", " lambda temp_profile: [calculate_saturation_vapor_pressure(temp) for temp in temp_profile]\n", ")\n", "\n", "# Calculate vapor pressure using relative humidity and saturation vapor pressure\n", "df['vapor_pressure'] = df.apply(\n", " lambda row: [rh * es / 100 for rh, es in zip(row['rel_humidity_profile'], row['saturation_vapor_pressure'])],\n", " axis=1\n", ")\n", "\n", "# Calculate specific humidity for every altitude and time step\n", "df['specific_humidity'] = df.apply(\n", " lambda row: [calculate_specific_humidity(ev, p) for ev, p in zip(row['vapor_pressure'], row['calculated_pressures'])],\n", " axis=1\n", ")\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "bec7b5b0", "metadata": {}, "outputs": [], "source": [ "# Apply the theta_v calculation to each row in the DataFrame\n", "df['theta_v'] = df.apply(calculate_theta_v_alternative_by_altitude, axis=1)\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "92333a6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " timestamp temperature_altitudes \\\n", "0 2024-05-02 00:00:01.502 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "1 2024-05-02 00:00:04.422 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "2 2024-05-02 00:00:07.332 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "3 2024-05-02 00:00:10.252 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "4 2024-05-02 00:00:13.182 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "\n", " temperature_profile \\\n", "0 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "1 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "2 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "3 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "4 [288.33844, 288.598, 288.94205, 289.4582, 289.... \n", "\n", " humidity_altitudes \\\n", "0 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "1 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "2 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "3 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "4 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "\n", " abs_humidity_profile \\\n", "0 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "1 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "2 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "3 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "4 [8.8963785, 8.770698, 8.597432, 8.339388, 8.11... \n", "\n", " rel_humidity_profile surf_temp surf_pres \\\n", "0 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "1 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "2 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "3 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "4 [73.60945, 71.431435, 68.36205, 64.19751, 60.9... 288.859985 1001.700012 \n", "\n", " calculated_pressures \\\n", "0 [1001.7000122070312, 1000.513945284491, 998.73... \n", "1 [1001.7000122070312, 1000.513945284491, 998.73... \n", "2 [1001.7000122070312, 1000.513945284491, 998.73... \n", "3 [1001.7000122070312, 1000.513945284491, 998.73... \n", "4 [1001.7000122070312, 1000.513945284491, 998.73... \n", "\n", " saturation_vapor_pressure \\\n", "0 [1724.9176489442978, 1753.9109403620173, 1793.... \n", "1 [1724.9176489442978, 1753.9109403620173, 1793.... \n", "2 [1724.9176489442978, 1753.9109403620173, 1793.... \n", "3 [1724.9176489442978, 1753.9109403620173, 1793.... \n", "4 [1724.9176489442978, 1753.9109403620173, 1793.... \n", "\n", " vapor_pressure \\\n", "0 [1269.7024166603194, 1252.8437468567492, 1225.... \n", "1 [1269.7024166603194, 1252.8437468567492, 1225.... \n", "2 [1269.7024166603194, 1252.8437468567492, 1225.... \n", "3 [1269.7024166603194, 1252.8437468567492, 1225.... \n", "4 [1269.7024166603194, 1252.8437468567492, 1225.... \n", "\n", " specific_humidity \\\n", "0 [7.921747364568807, 7.825375154142366, 7.66891... \n", "1 [7.921747364568807, 7.825375154142366, 7.66891... \n", "2 [7.921747364568807, 7.825375154142366, 7.66891... \n", "3 [7.921747364568807, 7.825375154142366, 7.66891... \n", "4 [7.921747364568807, 7.825375154142366, 7.66891... \n", "\n", " theta_v \n", "0 [289.7267196603122, 290.06875394587587, 290.53... \n", "1 [289.7267196603122, 290.06875394587587, 290.53... \n", "2 [289.7267196603122, 290.06875394587587, 290.53... \n", "3 [289.7267196603122, 290.06875394587587, 290.53... \n", "4 [289.7267196603122, 290.06875394587587, 290.53... \n" ] } ], "source": [ "print(df.head())" ] }, { "cell_type": "code", "execution_count": 22, "id": "8d8abe47", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " timestamp temperature_altitudes \\\n", "0 2024-05-02 00:00:00 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "1 2024-05-02 00:10:00 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "2 2024-05-02 00:20:00 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "3 2024-05-02 00:30:00 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "4 2024-05-02 00:40:00 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "\n", " temperature_profile \\\n", "0 [288.663330078125, 288.929931640625, 289.28396... \n", "1 [288.6338806152344, 288.9112854003906, 289.277... \n", "2 [288.0594177246094, 288.3624572753906, 288.764... \n", "3 [288.37603759765625, 288.5799865722656, 288.84... \n", "4 [288.52508544921875, 288.6865234375, 288.89154... \n", "\n", " humidity_altitudes \\\n", "0 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "1 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "2 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "3 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "4 [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16... \n", "\n", " abs_humidity_profile \\\n", "0 [8.944648742675781, 8.821794509887695, 8.65219... \n", "1 [9.089993476867676, 8.965066909790039, 8.79284... \n", "2 [9.27541446685791, 9.157119750976562, 8.993834... \n", "3 [9.293905258178711, 9.186347007751465, 9.03747... \n", "4 [9.350138664245605, 9.244071006774902, 9.09741... \n", "\n", " rel_humidity_profile surf_temp surf_pres \\\n", "0 [73.74308013916016, 71.57271575927734, 68.5101... 288.911926 1001.680603 \n", "1 [73.27970123291016, 71.10831451416016, 68.0383... 288.914948 1001.599915 \n", "2 [73.79426574707031, 71.64873504638672, 68.5985... 288.762482 1001.525085 \n", "3 [74.26726531982422, 72.14313507080078, 69.1157... 288.767334 1001.500427 \n", "4 [74.2414779663086, 72.11460876464844, 69.08547... 288.843262 1001.436951 \n", "\n", " calculated_pressures \\\n", "0 [1001.6805864352624, 1000.4958924785197, 998.7... \n", "1 [1001.5999755859375, 1000.4152762406966, 998.6... \n", "2 [1001.5251170191272, 1000.3381991361592, 998.5... \n", "3 [1001.5004876857851, 1000.3146966493741, 998.5... \n", "4 [1001.4369612068965, 1000.2517710289196, 998.4... \n", "\n", " saturation_vapor_pressure \\\n", "0 [1761.4554501459002, 1791.8581654754876, 1832.... \n", "1 [1758.286915238908, 1789.7639167397813, 1832.0... \n", "2 [1694.2489166997784, 1727.5522510558935, 1772.... \n", "3 [1729.2321283362905, 1751.916647421055, 1781.9... \n", "4 [1745.7798982612858, 1763.8612835745616, 1787.... \n", "\n", " vapor_pressure \\\n", "0 [1298.9735909251294, 1282.5040888060637, 1255.... \n", "1 [1288.4466975188855, 1272.6548405364863, 1246.... \n", "2 [1250.2608797971307, 1237.7689709065469, 1216.... \n", "3 [1284.2840847918062, 1263.9098336354416, 1231.... \n", "4 [1296.0972216038308, 1272.008624738708, 1234.6... \n", "\n", " specific_humidity \\\n", "0 [8.10543969773912, 8.011693961250709, 7.856820... \n", "1 [8.040085966403419, 7.950514645280254, 7.80020... \n", "2 [7.801244302196233, 7.732139232977686, 7.60938... \n", "3 [8.01477920941552, 7.8964112440758925, 7.70753... \n", "4 [8.089371127208365, 7.947752203769642, 7.72715... \n", "\n", " theta_v \n", "0 [290.0858133960694, 290.43592507855124, 290.91... \n", "1 [290.0447632764749, 290.4057216134137, 290.894... \n", "2 [289.4255323526884, 289.8154840363391, 290.345... \n", "3 [289.7809497745881, 290.0628594013046, 290.444... \n", "4 [289.9441129152462, 290.1788120501466, 290.493... \n" ] } ], "source": [ "# Function to compute the mean profile for arrays\n", "def compute_mean_profile(profiles):\n", " if len(profiles) == 0:\n", " return []\n", " profiles_array = np.array(profiles) # Convert to numpy array\n", " return np.mean(profiles_array, axis=0).tolist() # Convert back to list for JSON-friendly output\n", "\n", "# Resample and average function\n", "def resample_and_average(df, interval='10T'):\n", " resampled = df.resample(interval).agg({\n", " 'temperature_altitudes': 'first', # Assuming this does not change often\n", " 'temperature_profile': lambda x: compute_mean_profile(list(x)),\n", " 'humidity_altitudes': 'first', # Same assumption\n", " 'abs_humidity_profile': lambda x: compute_mean_profile(list(x)),\n", " 'rel_humidity_profile': lambda x: compute_mean_profile(list(x)),\n", " 'surf_temp': 'mean', # Average surface temperature\n", " 'surf_pres': 'mean', # Average surface pressure\n", " 'calculated_pressures': lambda x: compute_mean_profile(list(x)),\n", " 'saturation_vapor_pressure': lambda x: compute_mean_profile(list(x)),\n", " 'vapor_pressure': lambda x: compute_mean_profile(list(x)),\n", " 'specific_humidity': lambda x: compute_mean_profile(list(x)),\n", " 'theta_v': lambda x: compute_mean_profile(list(x)),\n", " })\n", " return resampled\n", "\n", "df['timestamp'] = pd.to_datetime(df['timestamp'])\n", "# Set 'timestamp' as the index\n", "df.set_index('timestamp', inplace=True)\n", "\n", "\n", "# Apply the resampling function\n", "df_10min_avg = resample_and_average(df) # Adjust interval as needed\n", "\n", "# Reset index if you want 'timestamp' back as a column\n", "df_10min_avg.reset_index(inplace=True)\n", "\n", "# Display the first few rows of the averaged DataFrame\n", "print(df_10min_avg.head())\n" ] }, { "cell_type": "code", "execution_count": 23, "id": "b4f6fa9d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved DataFrame to: C:\\Users\\magda\\Master_Thesis\\Cloud_radar\\2024-05\\2024-05-02\\cloud_radar_vertical_dataset_10min.parquet\n" ] } ], "source": [ "\n", "# 1. Build the output‐path inside the same folder\n", "parquet_path = os.path.join(day_folder_path, \"cloud_radar_vertical_dataset_10min.parquet\")\n", "\n", "# 2. Save `df` to Parquet\n", "df_10min_avg.to_parquet(parquet_path, engine=\"pyarrow\", index=False)\n", "\n", "print(f\"Saved DataFrame to: {parquet_path}\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "e6ff7055", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "timestamp 2024-05-02 13:10:00\n", "temperature_altitudes [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16...\n", "temperature_profile [297.51214599609375, 296.8507995605469, 295.92...\n", "humidity_altitudes [0.0, 10.0, 25.0, 50.0, 75.0, 100.0, 130.0, 16...\n", "abs_humidity_profile [9.746323585510254, 9.563286781311035, 9.31776...\n", "rel_humidity_profile [72.02877807617188, 69.86646270751953, 66.8326...\n", "surf_temp 297.160004\n", "surf_pres 999.460327\n", "calculated_pressures [999.4603111995524, 998.3115719370512, 996.586...\n", "saturation_vapor_pressure [3047.361427449103, 2928.6822932408813, 2769.4...\n", "vapor_pressure [2194.99464661329, 2046.1819259219317, 1850.89...\n", "specific_humidity [13.773983435360687, 12.847753691932454, 11.63...\n", "theta_v [300.00301077120986, 299.2667319808903, 298.26...\n", "Name: 79, dtype: object\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# Assuming df_10min_avg is your averaged DataFrame and it's structured correctly.\n", "# Select the 10th entry in the averaged DataFrame\n", "example_data = df_10min_avg.iloc[79] # Indexing starts at 0, so 9 is the 10th entry\n", "print(example_data)\n", "# Create subplots in one row\n", "fig, axs = plt.subplots(1, 4, figsize=(20, 5), sharey=True)\n", "\n", "# Extracting the altitude array (assuming it's the same for all parameters)\n", "altitude = np.array(example_data['temperature_altitudes']) # Adjusted for altitude data\n", "\n", "# Plot Temperature\n", "axs[0].scatter(example_data['temperature_profile'], altitude, label='Temperature (K)', color='red')\n", "axs[0].set_title('Temperature (K)', fontsize=14)\n", "axs[0].set_xlabel('Value')\n", "axs[0].grid()\n", "\n", "# Plot Theta_v\n", "axs[1].scatter(example_data['theta_v'], altitude, label='Theta_v (K)', color='orange')\n", "axs[1].set_title('Theta_v (K)', fontsize=14)\n", "axs[1].set_xlabel('Value')\n", "axs[1].grid()\n", "\n", "# Plot Relative Humidity (RH)\n", "axs[2].scatter(example_data['rel_humidity_profile'], altitude, label='Relative Humidity (%)', color='blue')\n", "axs[2].set_title('Relative Humidity (%)', fontsize=14)\n", "axs[2].set_xlabel('Value')\n", "axs[2].grid()\n", "\n", "# Plot Specific Humidity (qv)\n", "axs[3].scatter(example_data['specific_humidity'], altitude, label='Specific Humidity (g/kg)', color='green')\n", "axs[3].set_title('Specific Humidity (g/kg)', fontsize=14)\n", "axs[3].set_xlabel('Value')\n", "axs[3].grid()\n", "\n", "# Set shared y-axis limits\n", "axs[0].set_ylabel('Altitude (m)')\n", "\n", "plt.tight_layout()\n", "\n", "# Add a horizontal line at 2 km\n", "for ax in axs:\n", " ax.axhline(y=2000, color='red', linestyle='--')\n", " ax.set_ylabel('Altitude (m)')\n", "#plt.ylim(0,2000)\n", "plt.suptitle(f'Profile at Time: {example_data[\"timestamp\"]}', fontsize=16, y=1.02) # Using example_data.name for the timestamp\n", "plt.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }