# Utilizing NOAA (National Oceanic and Atmospheric Administration) Gridded Binary data for determining fuel efficient IFR routes for Ultra Long Range Flight Operations

> Using NOAA gridded binary data to determine fuel-efficient IFR routes for ultra long range flight operations.

[Paper page](https://vsl-landing-page-el7cn5p1w-ifly-leonards-projects.vercel.app/research/noaa-gridded-binary-fuel-efficient-routes)

## Metadata

- Authors: Leonard Selvaraja Fernando
- Year: 2023
- Organization: Workplace Tinkering
- Type: Research Article
- Category: Research Paper
- Status: Published

## Abstract

This research explores the application of NOAA's Gridded Binary (GRIB) meteorological data in optimizing fuel-efficient Instrument Flight Rules (IFR) routes for Ultra Long Range (ULR) flight operations. Traditional flight planning relies on static wind models and historical averages, often leading to suboptimal fuel consumption on routes exceeding 14 hours. By ingesting real-time GRIB2 data into a custom routing algorithm, this study demonstrates potential fuel savings of 3–7% on transpacific and transatlantic ULR routes. The algorithm processes upper-air wind, temperature, and pressure data at multiple flight levels, dynamically adjusting the route to exploit favorable wind patterns and avoid adverse conditions. A case study using 12 months of historical GRIB data on the Singapore–Newark route validates the approach, showing consistent fuel efficiency gains across seasonal variations.

## Proposed Hypotheses

- H0: Real-time GRIB2 meteorological data does not produce significantly different fuel-optimal routes compared to static wind models for ULR flight operations.
- H1: Ingesting real-time GRIB2 upper-air wind data into a dynamic routing algorithm yields measurable fuel savings of at least 3% on transpacific ULR routes.
- H2: The fuel efficiency gains from GRIB-based dynamic routing exhibit statistically significant variation across seasonal weather patterns on the Singapore–Newark route.

## Table of Contents

1. Introduction
2. Background on GRIB Data
3. Methodology & Algorithm Design
4. Data Collection & Processing Pipeline
5. Route Optimization Framework
6. Case Study: Singapore–Newark ULR Route
7. Results & Validation
8. Operational Considerations
9. Conclusion & Future Work

## Data Collection Method

Algorithmic Modeling & Historical Validation: 12 months of historical GRIB2 meteorological data (upper-air wind, temperature, pressure) collected from NOAA's NOMADS server. A custom Python-based routing algorithm processed data at 13 flight levels (FL280–FL400) across 365 daily datasets for the Singapore–Newark ULR route.

## Tools Used

Not specified.

## Meta

Canonical LLMs file: https://vsl-landing-page-el7cn5p1w-ifly-leonards-projects.vercel.app/research/noaa-gridded-binary-fuel-efficient-routes/llms.txt