Python: Detecting the Vehicle on the real-time footage

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#!/usr/bin/env python3 """ Vehicle Detector with Timestamp Logging Enhanced version: Logs timestamps for each vehicle and saves to daily log files """ import cv2 import numpy as np from picamera2 import Picamera2 import time import os from datetime import datetime, timedelta import json class VehicleDetectorLogger: def __init__(self): # Create log directory self.log_dir = "vehicle_logs" os.makedirs(self.log_dir, exist_ok=True) # Current log file self.current_log_date = datetime.now().date() self.log_file = self.get_log_file_path() # Initialize statistics self.vehicle_count = 0 self.vehicle_records = [] # Store all vehicle records self.daily_stats = self.load_daily_stats() # Detection parameters self.last_count_time = time.time() self.frame_count = 0 self.start_time = time.time() # Initialize camera print("Initializing camera...") self.picam2 = Picamera2() config = self.picam2.create_video_configuration( main={"size": (640, 480), "format": "RGB888"} ) self.picam2.configure(config) # Background subtractor self.fgbg = cv2.createBackgroundSubtractorMOG2(history=50, varThreshold=25) print("System ready!") print(f"Log file: {self.log_file}") print("-" * 50) def get_log_file_path(self): """Get log file path for current date""" date_str = datetime.now().strftime("%Y%m%d") return os.path.join(self.log_dir, f"vehicles_{date_str}.txt") def load_daily_stats(self): """Load daily statistics""" stats_file = os.path.join(self.log_dir, "daily_stats.json") if os.path.exists(stats_file): try: with open(stats_file, 'r') as f: stats = json.load(f) # Keep only last 7 days of data seven_days_ago = (datetime.now() - timedelta(days=7)).strftime("%Y%m%d") stats = {k: v for k, v in stats.items() if k >= seven_days_ago} return stats except: return {} return {} def save_daily_stats(self): """Save daily statistics""" stats_file = os.path.join(self.log_dir, "daily_stats.json") date_str = datetime.now().strftime("%Y%m%d") self.daily_stats[date_str] = { "total_vehicles": self.vehicle_count, "records_count": len(self.vehicle_records), "date": datetime.now().strftime("%Y-%m-%d") } with open(stats_file, 'w') as f: json.dump(self.daily_stats, f, indent=2) def log_vehicle(self, vehicle_id, timestamp, position=None): """Log vehicle detection to file""" # Check if need to switch to new day's log file current_date = datetime.now().date() if current_date != self.current_log_date: self.current_log_date = current_date self.log_file = self.get_log_file_path() print(f"New day! Switching to log file: {self.log_file}") # Format timestamp time_str = timestamp.strftime("%Y-%m-%d %H:%M:%S.%f")[:-3] # Create record record = { "id": vehicle_id, "timestamp": time_str, "unix_time": timestamp.timestamp(), "date": timestamp.strftime("%Y-%m-%d"), "time": timestamp.strftime("%H:%M:%S"), "position": position } # Add to records list self.vehicle_records.append(record) # Save to text file with open(self.log_file, 'a') as f: log_line = f"[{time_str}] Vehicle #{vehicle_id:04d} detected" if position: log_line += f" at position {position}" log_line += "\n" f.write(log_line) # Also save to JSON file (for easy analysis) json_file = self.log_file.replace('.txt', '.json') with open(json_file, 'w') as f: json.dump(self.vehicle_records, f, indent=2) # Print to console print(f"[{timestamp.strftime('%H:%M:%S')}] 🚗 Vehicle #{vehicle_id:04d} detected") return record def detect_and_count(self, frame): """Detect and count vehicles""" # Convert to grayscale gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Apply background subtraction fgmask = self.fgbg.apply(gray) # Morphological operations kernel = np.ones((5,5), np.uint8) fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel) fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel) # Find contours contours, _ = cv2.findContours(fgmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) detected_vehicles = [] for contour in contours: area = cv2.contourArea(contour) if area > 1000: # Only process large enough areas x, y, w, h = cv2.boundingRect(contour) # Only focus on bottom half of screen if y > 240 and w > 50 and h > 50: # Calculate center point center_x = x + w // 2 center_y = y + h // 2 # Check if new vehicle (avoid duplicate counting) current_time = time.time() if current_time - self.last_count_time > 2.0: self.vehicle_count += 1 self.last_count_time = current_time # Log vehicle timestamp = datetime.now() position = {"x": center_x, "y": center_y, "w": w, "h": h} record = self.log_vehicle(self.vehicle_count, timestamp, position) detected_vehicles.append({ "bbox": (x, y, w, h), "center": (center_x, center_y), "area": area, "record": record }) else: # Just draw, don't count detected_vehicles.append({ "bbox": (x, y, w, h), "center": (center_x, center_y), "area": area, "record": None }) return detected_vehicles, fgmask def draw_detection_info(self, frame, vehicles, fgmask): """Draw detection information on frame""" # Draw detected vehicles for vehicle in vehicles: x, y, w, h = vehicle["bbox"] center_x, center_y = vehicle["center"] # Draw bounding box color = (0, 255, 0) if vehicle["record"] else (0, 200, 200) thickness = 2 if vehicle["record"] else 1 cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness) # Draw center point cv2.circle(frame, (center_x, center_y), 4, (0, 0, 255), -1) # If counted vehicle, show ID if vehicle["record"]: cv2.putText(frame, f"#{vehicle['record']['id']}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) # Draw detection line detection_line_y = 320 cv2.line(frame, (0, detection_line_y), (640, detection_line_y), (0, 255, 255), 2) cv2.putText(frame, "Detection Line", (10, detection_line_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1) # Display statistics stats_y = 30 cv2.putText(frame, f"Vehicles: {self.vehicle_count}", (10, stats_y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) # Display FPS self.frame_count += 1 if self.frame_count % 30 == 0: fps = self.frame_count / (time.time() - self.start_time) cv2.putText(frame, f"FPS: {fps:.1f}", (10, stats_y + 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2) # Display current time current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") cv2.putText(frame, current_time, (350, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2) # Display today's date date_str = datetime.now().strftime("%Y-%m-%d") cv2.putText(frame, f"Today: {date_str}", (350, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2) # Display log status log_status = f"Log: {os.path.basename(self.log_file)}" cv2.putText(frame, log_status, (10, stats_y + 80), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (200, 200, 255), 1) return frame def generate_daily_report(self): """Generate daily report""" if not self.vehicle_records: return # Count by hour hourly_counts = {} for record in self.vehicle_records: hour = datetime.fromtimestamp(record["unix_time"]).strftime("%H:00") hourly_counts[hour] = hourly_counts.get(hour, 0) + 1 # Generate report file report_file = self.log_file.replace('.txt', '_report.txt') with open(report_file, 'w') as f: f.write("=" * 60 + "\n") f.write(f"Daily Vehicle Detection Report\n") f.write(f"Date: {datetime.now().strftime('%Y-%m-%d')}\n") f.write("=" * 60 + "\n\n") f.write(f"Total Vehicles Detected: {self.vehicle_count}\n") f.write(f"Detection Start Time: {datetime.fromtimestamp(self.start_time).strftime('%Y-%m-%d %H:%M:%S')}\n") f.write(f"Report Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n") f.write("Hourly Statistics:\n") f.write("-" * 30 + "\n") for hour in sorted(hourly_counts.keys()): f.write(f"{hour}: {hourly_counts[hour]} vehicles\n") f.write("\nDetailed Records:\n") f.write("-" * 60 + "\n") for i, record in enumerate(self.vehicle_records, 1): f.write(f"{i:3d}. [{record['time']}] Vehicle #{record['id']:04d}\n") print(f"Daily report generated: {report_file}") def run(self): """Main detection loop""" print("Vehicle Detection System Starting") print("Controls:") print(" 'q' - Quit program") print(" 'r' - Reset counter") print(" 's' - Save current frame") print(" 'p' - Generate daily report") print(" '+' - Increase sensitivity") print(" '-' - Decrease sensitivity") print("-" * 50) # Start camera self.picam2.start() time.sleep(1) # Let camera stabilize try: while True: # Capture frame frame = self.picam2.capture_array() frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) # Detect and count vehicles vehicles, fgmask = self.detect_and_count(frame) # Draw detection info frame = self.draw_detection_info(frame, vehicles, fgmask) # Display frames cv2.imshow("Vehicle Detector with Logger", frame) cv2.imshow("Motion Mask", fgmask) # Handle keyboard input key = cv2.waitKey(1) & 0xFF if key == ord('q'): print("\nQuitting program") break elif key == ord('r'): self.vehicle_count = 0 self.vehicle_records.clear() self.last_count_time = time.time() self.fgbg = cv2.createBackgroundSubtractorMOG2(history=50, varThreshold=25) print("Counter reset") elif key == ord('s'): timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"snapshot_{timestamp}.jpg" cv2.imwrite(filename, frame) print(f"Frame saved: {filename}") elif key == ord('p'): self.generate_daily_report() elif key == ord('+'): # Increase sensitivity self.fgbg.setVarThreshold(max(10, self.fgbg.getVarThreshold() - 5)) print(f"Sensitivity increased: varThreshold={self.fgbg.getVarThreshold()}") elif key == ord('-'): # Decrease sensitivity self.fgbg.setVarThreshold(min(100, self.fgbg.getVarThreshold() + 5)) print(f"Sensitivity decreased: varThreshold={self.fgbg.getVarThreshold()}") except KeyboardInterrupt: print("\nUser interrupted") finally: # Cleanup resources self.picam2.stop() cv2.destroyAllWindows() # Save statistics self.save_daily_stats() # Generate final report self.generate_daily_report() # Output summary total_time = time.time() - self.start_time fps = self.frame_count / total_time if total_time > 0 else 0 print("\n" + "=" * 60) print("Detection System Stopped") print("=" * 60) print(f"Total Runtime: {total_time:.1f} seconds") print(f"Frames Processed: {self.frame_count}") print(f"Average FPS: {fps:.1f}") print(f"Total Vehicles Detected: {self.vehicle_count}") print(f"Log File: {self.log_file}") print(f"JSON Data: {self.log_file.replace('.txt', '.json')}") print("=" * 60) def main(): """Main function""" print("=" * 60) print("Raspberry Pi Vehicle Detection System - With Logging") print("=" * 60) # Create detector and run detector = VehicleDetectorLogger() detector.run() if __name__ == "__main__": main()

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Matlab: Hysplit Toolkit

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Hysplit: CONC.CFG

Breakdown of Parameters in CONC.CFG

The CONC.CFG file is a crucial configuration file that controls advanced settings for the HYSPLIT concentration (dispersion) model. It defines how calculations are performed and what output is generated, complementing the CONTROL file used in your MATLAB script.

Here's a breakdown of the CONC.CFG file structure and parameters:

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&SETUP tratio = 0.75, delt = 0.0, initd = 0, kpuff = 0, khmax = 9999, khinp = 0, numpar = 2500, maxpar = 10000, nbptyp = 1, qcycle = 0.0, efile = '', k10m = 1, kdef = 0, krand = 2, kzmix = 0, kbls = 1, kblt = 0, isot = -99, idsp = 1, wvert = .TRUE., plrise = 1, area = 0, vscale = 200.0, vscales = 5.0, vscaleu = 200.0, hscale = 10800.0, capemin = -1.0, tkemin = 0.001, uratio = 5.873300076, tvmix = 1.00, tkerd = 0.18, tkern = 0.18, kmix0 = 150, kmixd = 0, ninit = 1, ndump = 0, ncycl = 0, pinbc = 'PARINBC', pinpf = 'PARINIT', poutf = 'PARDUMP', messg = 'MESSAGE', vdist = 'VMSDIST', mgmin = 10, conage = 24, gemage = 48, kmsl = 0, kwet = 1, ichem = 0, cpack = 1, cmass = 0, kspl = 1, krnd = 6, frhmax = 3.00, splitf = 1.00, frhs = 1.00, frvs = 0.01, frts = 0.10, dxf = 1.00, dyf = 1.00, dzf = 0.01, /

Parameter Explanations

Core Simulation Parameters

• tratio = 0.75 - Time step ratio (stability factor). HYSPLIT adjusts its internal time step so a particle/puff doesn't travel more than 75% of a grid cell in one step. Potential values: 0.0 to 1.0 (typically 0.5–0.9). Affects how your pollutant (e.g., SO2) disperses in the atmosphere.
• delt = 0.0 - Fixed time step (minutes). 0.0 means HYSPLIT uses a variable time step controlled by tratio. Potential values: 0.0 (auto) or positive values (e.g., 1.0). Ensures efficient computation for your 48-hour simulation.
• initd = 0 - Particle/puff initialization method. 0 = 3D particle mode (pure Lagrangian, tracks individual particles), 1 = Gaussian horizontal puff, vertical particle, 2 = Top-hat horizontal puff, vertical particle, 3 = Gaussian puff (3D), 4 = Top-hat puff (3D). Suitable for detailed dispersion tracking in your SO2 simulation.
• kpuff = 0 - Horizontal puff growth option. 0 = Linear growth with time, 1 = Growth proportional to square root of time. Ignored since initd = 0. Not applicable in your current setup.
• khmax = 9999 - Maximum duration (hours) for a particle/puff. 9999 means "no limit". Potential values: Positive integers. Matches your 48-hour forward run (param.runTime).
• khinp = 0 - Input flag for puff splitting. 0 = No special input. Typically unused unless puff-splitting files are used. Not relevant unless customizing puff behavior.

Particle and Emission Parameters

• numpar = 2500 - Number of particles or puffs released per emission cycle. Potential values: Positive integers (e.g., 1000–10000). Controls how finely your SO2 emission (param.emissionRate = 1000 kg/hr) is resolved.
• maxpar = 10000 - Maximum total number of particles/puffs allowed. Potential values: Positive integers (e.g., 1000–100000). Ensures your simulation doesn’t crash due to too many particles.
• nbptyp = 1 - Number of bytes per particle in the output file (affects PARDUMP file size). Potential values: 1, 2, or 4. Affects the PARDUMP file if used (not in your script’s output).
• qcycle = 0.0 - Emission cycle time (hours). 0.0 = Continuous emission during emissionHours. Potential values: 0.0 or positive values (e.g., 1.0 for hourly pulses). Matches your 1-hour emission event (param.emissionHours = 1).
• efile = '' - Path to an emission input file for complex source configurations. Potential values: Empty string ('') or a file path. Not needed since your script specifies param.emissionRate.

Turbulence and Boundary Layer Parameters

• k10m = 1 - Use 10m wind data for turbulence calculations. Potential values: 0 (no), 1 (yes). Enhances accuracy near your 10m emission altitude (alt = 10).
• kdef = 0 - Horizontal turbulence deformation method. 0 = Based on velocity variances, 1 = Based on deformation field. Suitable for your general-purpose SO2 dispersion.
• krand = 2 - Random number generator for turbulence. Potential values: 1, 2, or 3. 2 is robust for your run.
• kzmix = 0 - Vertical mixing method. 0 = Use meteorology data, 1 = Scale by boundary layer depth. Matches your use of GDAS1 files in metPath.
• kbls = 1 - Boundary layer stability method. 0 = Use input meteorology, 1 = Compute from heat and momentum fluxes. Enhances realism for your SO2 dispersion.
• kblt = 0 - Boundary layer turbulence parameterization. 0 = Beljaars-Holtslag for stable, Kanthar-Clayson for unstable, 1 = Shortwave radiation-based. Good default for your simulation.
• isot = -99 - Isotropic turbulence flag. -99 = Disabled. Ignored in your run, safe to leave as is.
• idsp = 1 - Dispersion method. 0 = No dispersion (advection only), 1 = Dispersion with mixing. Essential for modeling SO2 spread in your script.
• wvert = .TRUE. - Enable vertical velocity in dispersion calculations. Potential values: .TRUE., .FALSE. Enhances vertical dispersion in your simulation.
• plrise = 1 - Plume rise calculation method. 0 = No plume rise, 1 = Briggs plume rise. Suitable if your SO2 source is a point source like a factory.
• area = 0 - Area source size (m²). 0 = Point source. Potential values: 0 or positive value. Matches your single-point emission (lat = 37.5, lon = -120.0, alt = 10).
• vscale, vscales, vscaleu = 200.0, 5.0, 200.0 - Turbulence velocity scales (m/s) for general, stable, and unstable conditions. Potential values: Positive values (e.g., 5.0–500.0). Affects how widely your SO2 disperses vertically.
• hscale = 10800.0 - Horizontal turbulence scale (seconds). Potential values: Positive values (e.g., 1000–10800). Influences horizontal spread of pollutants in your simulation.
• capemin = -1.0 - Minimum convective available potential energy (J/kg) for plume rise. -1.0 = Disabled. Simplifies your run, relying on plrise.
• tkemin = 0.001 - Minimum turbulent kinetic energy (m²/s²). Potential values: Small positive values (e.g., 0.001–0.1). Ensures turbulence in calm conditions.
• uratio = 5.873300076 - Ratio of wind speed to turbulence velocity. Potential values: Positive values (default ~5.87). Affects turbulence; default is fine.
• tvmix = 1.00 - Vertical mixing coefficient. Potential values: 0.0–1.0. Ensures realistic vertical dispersion.
• tkerd, tkern = 0.18, 0.18 - Daytime and nighttime turbulence ratios. Potential values: 0.1–0.5. Suitable for your run.
• kmix0 = 150 - Minimum mixing depth (m). Potential values: Positive integers (e.g., 50–500). Reasonable for near-surface emissions.

Initialization and Output Control

• kmixd = 0 - Mixing depth source. 0 = Use met data, 1 = Compute internally. Matches your reliance on GDAS1 files.
• ninit = 1 - Initialization of particles from PARINIT file. 0 = None, 1 = Read PARINIT if exists. Not used unless you specify pinpf.
• ndump = 0 - Interval (hours) for writing PARDUMP file. 0 = None. Potential values: Positive integers. Reduces output file size in your run.
• ncycl = 0 - Particle release cycle interval (hours). 0 = Continuous. Consistent with your 1-hour emission.
• pinbc = 'PARINBC' - Filename for boundary condition input. Potential values: File names or empty. Not used in your script.
• pinpf = 'PARINIT' - Filename for particle initialization. Potential values: File names or empty. Not used in your script.
• poutf = 'PARDUMP' - Filename for particle dump output. Potential values: File names or empty. Not used in your script.
• messg = 'MESSAGE' - Names the detailed log file generated by HYSPLIT. Potential values: File name or empty. Default is fine.
• vdist = 'VMSDIST' - Vertical distribution method. Potential values: String (default 'VMSDIST'). Controls vertical particle distribution; default is standard.
• mgmin = 10 - Minimum number of particles per grid cell for concentration output. Potential values: Positive integers. Ensures statistical reliability in your grid.
• conage = 24 - Maximum age (hours) for concentration calculations. Potential values: Positive integers. Fits your 48-hour run, limiting older contributions.
• gemage = 48 - Maximum age (hours) for particle transport. Potential values: Positive integers. Matches your runTime, ensuring particles don’t persist beyond your simulation.
• kmsl = 0 - Altitude input type. 0 = Meters above ground level (AGL), 1 = Meters above sea level (ASL). Matches your MATLAB input (alt = 10).

Deposition and Chemistry

• kwet = 1 - Enable wet deposition. 0 = Off, 1 = On. Ignored unless you modify HYSPLIT_writeControlConc.m to include deposition parameters.
• ichem = 0 - Chemistry module. 0 = None. Potential values: Other integers (see HYSPLIT docs). Suitable for your simple SO2 simulation.

Output File Parameters

• cpack = 1 - Concentration output packing. 0 = No packing, 1 = Packed binary output. Matches your cdump_test output.
• cmass = 0 - Concentration output type. 0 = Concentration (e.g., kg/m³), 1 = Mass loading (e.g., kg). Appropriate for analyzing SO2 concentrations.
• kspl = 1 - Particle/puff splitting interval (hours). 0 = No splitting. Enhances accuracy in your simulation.
• krnd = 6 - Random number seed control. Potential values: Positive integers. Ensures reproducible results.
• frhmax = 3.00 - Maximum horizontal puff growth factor. Potential values: Positive values (e.g., 1.0–5.0). Ignored since initd = 0.
• splitf, frhs, frvs, frts = 1.00, 1.00, 0.01, 0.10 - Splitting factors for puffs (horizontal, vertical, time). Potential values: Positive values. Ignored since initd = 0.
• dxf, dyf, dzf = 1.00, 1.00, 0.01 - Scaling factors for horizontal and vertical components of concentration output. Potential values: Positive values. Matches your grid settings (gridSpacing = 0.1).

Note: This configuration is set up for a standard concentration simulation with 2500 particles, continuous emission for 1 hour, and output in a packed binary cdump file. It uses 3D particle mode (initd = 0) for precise dispersion tracking, suitable for your SO2 simulation in MATLAB.

HYSPLIT CONC.CFG Configuration Guide | Understanding HYSPLIT Concentration Model Settings

Hysplit: TRAJ.CFG

Understanding the HYSPLIT TRAJ.CFG Configuration File

The TRAJ.CFG file is a crucial configuration file that controls advanced settings for the HYSPLIT trajectory model. It defines how calculations are performed and what output is generated.

Here's a breakdown of the TRAJ.CFG file structure and parameters:

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&SETUP tratio = 0.75, delt = 0.0, mgmin = 10, khmax = 9999, kmixd = 0, kmsl = 0, kagl = 1, k10m = 1, nstr = 0, mhrs = 9999, nver = 0, tout = 60, tm_pres = 1, tm_tpot = 0, tm_tamb = 0, tm_rain = 0, tm_mixd = 0, tm_relh = 0, tm_sphu = 0, tm_mixr = 0, tm_dswf = 0, tm_terr = 0, tm_uwnd = 0, tm_vwnd = 0, dxf = 1.00, dyf = 1.00, dzf = 0.01, messg = 'MESSAGE', /

Parameter Explanations

Core Simulation Parameters

• tratio = 0.75 - Time step ratio (stability factor). HYSPLIT adjusts its internal time step so an air parcel doesn't travel more than 75% of a grid cell in one step.
• delt = 0.0 - Fixed time step (minutes). 0.0 means HYSPLIT uses variable time step controlled by tratio.
• mgmin = 10 - Minimum number of grid cells for met data. Sanity check to prevent errors with bad met files.
• khmax = 9999 - Maximum duration (hours) for a trajectory. 9999 means "no limit".

Vertical Motion & Starting Height

• kmixd = 0 - Boundary Layer option. 0 = Ignore mixing depth.
• kmsl = 0 - Input starting height reference. 0 = meters above ground level (AGL).
• kagl = 1 - Output height reference. 1 = meters above ground level (AGL).
• k10m = 1 - Use 10m meteorological data for surface-based trajectories.

Output Control

• nstr = 0 - Number of special starting locations. 0 means not used.
• mhrs = 9999 - Maximum duration for meteorological data. 9999 means "no limit".
• nver = 0 - Vertical motion output flag. 0 = Do not include vertical velocity.
• tout = 60 - Output frequency. 60 = Save trajectory position every 60 minutes.

Meteorological Output Variables (tm_ flags)

These control what additional meteorological data is written to the output file:

• tm_pres = 1 - Pressure (hPa or mb) - INCLUDED
• tm_tpot = 0 - Potential Temperature (K) - Excluded
• tm_tamb = 0 - Ambient Temperature (K) - Excluded
• tm_rain = 0 - Rainfall/Precipitation - Excluded
• tm_mixd = 0 - Mixing Depth (m) - Excluded
• tm_relh = 0 - Relative Humidity (%) - Excluded
• tm_sphu = 0 - Specific Humidity (g/kg) - Excluded
• tm_mixr = 0 - Water Mixing Ratio (g/kg) - Excluded
• tm_dswf = 0 - Downward ShortWave Radiation Flux (W/m²) - Excluded
• tm_terr = 0 - Terrain Height (m) - Excluded
• tm_uwnd = 0 - U-Wind Component (m/s) - Excluded
• tm_vwnd = 0 - V-Wind Component (m/s) - Excluded

Output File Scaling Factors

• dxf = 1.00, dyf = 1.00 - Scaling factors for horizontal components of trajectory dispersion.
• dzf = 0.01 - Scaling factor for vertical component of trajectory dispersion.

Log File Control

• messg = 'MESSAGE' - Names the detailed log file generated by HYSPLIT.

Note: This configuration is set up for a standard, single, backward trajectory with output in meters AGL, saved every 60 minutes, with pressure as the only additional meteorological variable.

HYSPLIT TRAJ.CFG Configuration Guide | Understanding HYSPLIT Model Settings