NEXRAD Radar Data Processing – Python Script to Generate Matched CSV Output
Budget: $30 – $250 USD
I'm looking for an experienced developer to build a Python script that downloads and processes NEXRAD Level 2 or Level 3 data from the AWS S3 Open Data Registry, then outputs a CSV file that exactly matches a provided sample in both structure and content.
Project Overview:
Input Source: NEXRAD Level 2 or Level 3 data from AWS S3 Open Data Registry.
Output Requirement: A CSV file that precisely replicates the sample provided — this includes:
-Matching column names
-Accurate data formatting and types
-Proper ordering and value ranges
-Comparable file size and data density
Derived Fields: If you're using Level 2 data, your algorithm must accurately calculate:
-Maximum hail size
-Severe probability (sevprob)
-Additional probability values (prob, etc.)
Preferred Tech: Python with Py-ART is preferred, but I'm open to alternatives if you can justify them based on performance or accuracy.
Testing: I will provide you with:
-A specific date range
-One or more radar site IDs
-The sample CSV file
You’ll be expected to test your script using this data and compare your output to the sample file to ensure it matches exactly.
Important Requirements:
-You may use AI tools to assist development, but the final output must be 100% accurate and fully aligned with the sample.
-This task requires hands-on experience with NEXRAD radar data. It’s not suitable for those without prior exposure to Level 2 or Level 3 datasets and radar product formats.
Qualification Questions (Required):
To assess your familiarity with NEXRAD data, please answer the following questions based strictly on your own knowledge:
1 - Based on the attached sample CSV, is the data stored in Level 2 or Level 3 NEXRAD on AWS S3? Which specific radar product or message type does it come from?
2 - Would you prefer Level 2 or Level 3 for this task, and why — particularly with respect to accuracy and data availability?
3 - If not Python, what other language or tool would you recommend for processing this data, and what are the advantages or trade-offs?
Project Overview:
Input Source: NEXRAD Level 2 or Level 3 data from AWS S3 Open Data Registry.
Output Requirement: A CSV file that precisely replicates the sample provided — this includes:
-Matching column names
-Accurate data formatting and types
-Proper ordering and value ranges
-Comparable file size and data density
Derived Fields: If you're using Level 2 data, your algorithm must accurately calculate:
-Maximum hail size
-Severe probability (sevprob)
-Additional probability values (prob, etc.)
Preferred Tech: Python with Py-ART is preferred, but I'm open to alternatives if you can justify them based on performance or accuracy.
Testing: I will provide you with:
-A specific date range
-One or more radar site IDs
-The sample CSV file
You’ll be expected to test your script using this data and compare your output to the sample file to ensure it matches exactly.
Important Requirements:
-You may use AI tools to assist development, but the final output must be 100% accurate and fully aligned with the sample.
-This task requires hands-on experience with NEXRAD radar data. It’s not suitable for those without prior exposure to Level 2 or Level 3 datasets and radar product formats.
Qualification Questions (Required):
To assess your familiarity with NEXRAD data, please answer the following questions based strictly on your own knowledge:
1 - Based on the attached sample CSV, is the data stored in Level 2 or Level 3 NEXRAD on AWS S3? Which specific radar product or message type does it come from?
2 - Would you prefer Level 2 or Level 3 for this task, and why — particularly with respect to accuracy and data availability?
3 - If not Python, what other language or tool would you recommend for processing this data, and what are the advantages or trade-offs?
Related categories:
Python
Data Processing
Web Scraping
Software Architecture
Data Extraction
Data Visualization
Data Analysis