# Presentation: 743763

## The NextCast Project

**The ****NextCast**** Project**

## Talk Overview

** ****NextCast System Description**
** ****Forecast Modules**
** ****Forecast Integrator**
** ****System Performance**
** ****Summary**

## NextCast In A Nutshell

* **provides timely, accurate, worldwide forecasts *
* **is dynamic and modular so as to easily adapt to a rapidly changing forecasting environment*
* **is applicable to a variety of forecast problems*
* **uses state-of-the-art scientific and engineering principles*
* **requires only modest computing systems and common data sources*

**An automated weather forecast system that:**

## Basic NextCast System Diagram

- Forecast Module A

- Forecast Module B

- Forecast Module C

- Data
- Ingest

- Integrator

***Basic NextCast System Diagram***

**.**
**.**
**.**

## Data  Requirements

** **Real-time access to NOAAport and NCEP server data
-  Observations (METARs and Synoptic reports)
-  Model Grids  (MRF, AVN, ETA)
-  MOS Guidance products (NGM, MRF, AVN)
-  Climatology

##  Hardware and Software

**2x450 MHz processors**
**1 GB Memory**
**0.25 TB Disk**

**Hardware : Unix Workstation**

**Software :**

**Languages: C++, Fortran, C, Python, Perl**
**Tools/Stds: Unidata LDM, Decoders, NetCDF**
**Other Re-used Code: **

- LAPACK Libraries
- Australian Met Bureau Libraries
- Glue Layer

## Current Implementation

** **Short-term system:  00 - 48 hr fcsts, 3 hr interval
-  Long-term system:  0 - 12 day fcsts, 6 hr interval
-  Forecasts updated every 3 hours with new data
-  Forecast generation is schedule driven (cron)
-  Tested with up to 4700 “core” sites (US and international)
-  Additional sites interpolated using deviation from climatology technique or linear interpolation

## Forecast Variables

_**Core**__ _
- Max/Min Temp
- POP24, POP6, QPF6
- Temp & dew point
- wind u-, v-, speed
- cloudiness
- Cnd. Prb. Snow, Ice, Rain
- Prob. Of Thunder
- Prob. Of Fog
- Visibility

_**Derived**_
-  RH
-  wind direction
-  weather icon
-  weather phrase

**Core variables must be verifiable!**

## Forecast Sites

-  A “Core” site is a site which has observations
-  An “Interpolated” site lacks observations and is derived from core site data
-  Core sites are thus METAR and synoptic reporting sites.

## Forecast Modules

** **Climatology Module
-  Persistence Modules
-  NWS MRF and NGM MOS Modules
-  Dynamic MOS (DMOS)

**Each module creates forecasts for the **
**same set of sites, variables and times**

## Dynamic MOS (DMOS)

-  Similar to NWS MOS, but regressions built dynamically
-  Linear regression-based statistical method using 100 days of model forecasts and observations
-  Can be applied to any NWP forecast model fairly easily
-  Current system uses MRF, AVN and ETA DMOS
-  DMOS equations updated once a week
-  Uses “default equations” if statistical model fails

## The DMOS Process

## DMOS Default Equations

- Default equations are substituted whenever the statistical methods fail to produce a suitable result.
- Default equations are combinations of one or more of the regressors.
- Several regressors were designed specifically as defaults.

## Basic NextCast System Diagram

- Forecast Module A

- Forecast Module B

- Forecast Module C

- Data
- Ingest

- Integrator

***Basic NextCast System Diagram***

**.**
**.**
**.**

## Forecast Integrator 

* *Combines forecasts from many forecast modules
* *Discovers the “best” combination of forecast modules for a given variable, location and forecast time
-  Computationally simple and robust
-  Can easily adapt to the addition of new modules or removal of obsolete modules

## Forecast Integrator Process

- Forecast Module B

- Forecast Module N

- Data
- Ingest

***Forecast Integrator Process***

- Observation History

- Forecast History

- Integrator
- Empirics

- Forecast
- Integrator

- Integrator
- Weights

- Forecast
- Products

## Forecast Integration Technique

- Adaptable, fuzzy-logic integration scheme
- Linear combination of input forecasts
- Integration different for each site, parameter, forecast valid time and time of day the forecast is created.
- 4 x 108 weights in the long-term system
- Weights updated daily using history of recent forecasts and observations

## Forecast Integrator

- Forecast error as function of W1 & W2

**W****2**

**W****1**

**W****1****(i)**

**W****2****(i)**

**Integration Step**

**0**

**1**

**1**

**0**

## Integrator Weights

## Forecast Output Steps

-  Derived variables calculated
-  Additional sites forecasted using spatial interpolation
-  Additional times forecasted using temporal interpolation
-  Final data format netCDF and/or insertion into database

**Post Processing steps prepare output**

## System Performance

-  System very much in infancy, performance metrics are few (< 1 year of forecasting history)
-  In general, results are encouraging
-  Integrated forecasts better than any one individual forecast
-  Forecast skill varies by variable, spatial and time domains

## Max Temperature Error 

***for October 1-31 2000***

## Max Temperature Error 

***for January 16-29, 2001***

## Max Temperature Error 

***for March 7 - April 5, 2001***

## Max Temperature Error 

***for June 15 - July 15, 2001***

## Temperature Error 

***for May 1 -30, 2001***

## Temperature Error 

***for Jun 1 -30, 2001***

## Temperature Error 

***for July 1 -30, 2001***

## Forecast Module 

***Temperature Comparison***

## Forecast Module 

***Forecast Module ***
***Cloud Cover Comparison***

## Forecast Module 

***Forecast Module ***
***Wind Speed Comparison***

## Summary

-  NextCast integrated forecasts appear to be an improvement over any single forecast module
-  The system runs on inexpensive hardware and uses industry standard software and freely available data
-  Once configured, runs completely automated
-  Dynamic MOS technique allows rapid adoption of new NWP models
-  New forecast modules can be added or removed with modest  effort

## Summary (cont)

-  Minimal history of forecasts for testing
-  Poorer performance in transition seasons
-  Default equations used too often in DMOS

## NextCast Future

-  Continue performance tests
-  Add new forecast modules (ECMWF DMOS)
-  Examine “ensemble DMOS” technique
-  Address seasonality performance issues
-  Move from deterministic to probabilistic system