# Presentation: 562694

## NASA’s Short-term Prediction and Research Transition (SPoRT) Center

**Presentation at Rapid Prototyping Workshop**
**April 19-20, 2006**
**Dr. Gary Jedlovec,  NASA/MSFC**
**Earth Science Office**
_***Mission of the SPoRT Center:***_*** Apply NASA measurement systems and unique Earth science research to improve the accuracy of short-term (0-24 hr) weather prediction at ***_***the regional and local scale***_***         (http://weather.msfc.nasa.gov/sport/)***

**The SPoRT Center serves as one of the research-to-operations (R2O) testbeds for the SMD’s **_***Weather focus area***_**  **

## NASA   SPoRT

- Center

- NASA  Relevance
-    and Partners

## Slide 3

## Matching Data to Customer

** ****link data to forecast problems**
** ****development and testing of new **
**     ****products for operations**
** ****integrate capabilities into in NWS / WFO’s **
**      **_**decision support system**_** and verify / **
**      ****validate performance**
** ****develop / conduct training**
** ****user feedback / interaction**
** ****benchmark products **

**Matching Data to Customer**

## Unique NASA Data to Operations

_**EOS satellite data**_** provided in *****near real-time***** to 6 WFOs (Huntsville since 2/2003) to address specific forecast issues**
**	*****convective initiation, morning minimum temperatures, fog and low cloud detection, sea/land breeze convection/temperatures, off-coast precipitation mapping, and coastal processes***
**MODIS high resolution visible/infrared imagery – derived products**
**4 times / day – 15-30 minutes latency, full resolution**
**8-10 channels available **_**simulating NPOESS and GOES-R capabilities**_
**TPW, LST/SST, cloud and fog products, composite imagery for smoke/haze detection, surface vegetation patterns and snow cover**
**benchmarked night-time cloud detection capability and snow cover map**
**AMSR-E products to coastal offices (Miami and Mobile)**
**15-20 minute latency, rain rate – off-shore precipitation mapping**
**Nowcasting**
**convective initiation products for thunderstorm development**
**flash density of total lightning (LMA) – relation to severe weather**

## Unique Modeling / Assimilation to Operations  

**Transitioned model products to WFOs**
**twice daily WRF runs provide supplemental information to NWS at 12km with focus over Southeastern US – 0-48hr suite of products**
**Initialized with MODIS 1km composite SSTs since Fall 2005**
**benchmarked performance in several WFOs – positive impact of QPF as supplemental product**
**transitioned WRF capabilities to SERVIR – model run by CATHALAC**
**Future transitional activities**
**testing assimilation of AIRS profiles of temperature and humidity within ADAS/WRF – impact of data in void regions**
**conducting high resolution (2 km) simulations to assess impact of MODIS SST composites – Project Columbia**
** ****coastal circulations**
** ****oceanic clouds and precipitation**
** ****hurricane forecasts – near real-time as part of MAP06**

## SPoRT – What it is not!  

**SPoRT provides end-to-end transition capabilities to move products from the research environment to operations.  SPoRT  **_**IS**_
**an end-to-end transition facility – identification of products, rapid prototyping, V&V, training, benchmarking**
**discipline / focus area specific  -- narrow focus (short-term, regional – draws on expertise)**
**customer-centric with specific (identified) users / problems**
**dynamic**
**SPoRT is **_**NOT**_
**a RPC clearing house (one-stop shop) for EOS data and products**

## Thoughts on “What a RPC should be”!  

**Discipline specific – “can’t do it all”  **
**Knowledgeable of the EOS science and data community **
**know what is out there and what can be done**
**have good ties to EOS science data (IT) teams**
**A resource (source) for customers**
** ****use of EOS products**
**data, tools, and formats**
**training**
**Flexible – “one size doesn’t / can’t fit all”**
**Forward looking – beyond EOS to GEOSS**

## SPoRT – Lessons Learned  

**Work the transition / prototyping at all levels (political & managerial)**
**Establish good end user relationship**
**match product / capability to end user needs**
**gain “buy-in” from customer**
**Products need to be **_**timely**_** and in customers DSS**
**Training is important for customer usage (do not just “throw products over the fence”)**
**Can’t do it all ---- look for ”low hanging fruit” and pick your opportunities**
**Stay flexible - need different paradigms for different customers**

## SERVIR Ecological / Disaster Forecasting

**SERVIR Ecological / Disaster Forecasting**

**CATHALAC, City of Knowledge, Panama**

**NASA/MSFC Huntsville, Alabama**

- RadarSat data indicating flooded regions (in red) from Hurricane Stan in Guatemala
- supports relief
- efforts

**Programmatic “buy-in”  at **_**all levels**_**  ensures success of rapid prototyping activities**

**Disaster agencies integrate relief information with GIS / flooding data**

## Examples of Observation, Modeling, and 

**Nowcasting Products at the WFOs**

## MODIS/AMSR-E Data Access in AWIPS

**Data  provided in D2D**
** ****access like GOES   **
**    ****satellite data**
** ****correspond to WFO **
**   ****coverage areas**
** ****at highest resolution**
**Examples:**

** ****color composites**

** ****fog product**

** ****morning min. temp.**

** ****TPW**

** ****SSTs**

** ****rain rates**

**  ****& composite SST**

**Previews available http://weather.msfc.nasa.gov/sport/sport_observations.html**

## Total Lightning Impacts Decision Making....

**Has directly contributed to several correct severe  warning decisions at HUN and BMX**
***“...******the LMA density map gives you a great overall view of where storms with intensifying updrafts are located.  So it gives you a good map of where to concentrate attention.”***
***“******I believe the flash density rates were the primary factor in holding off on a warning.”***
**Used in Warning Event Simulator (WES) for office training**

## Advanced Modeling and Data Assimilation

**Advanced Modeling and Data Assimilation**

**WRF - **_**Operational**_** (since 2/04)**
**12-km resolution & 28 vert. levels**
**12Z & 00Z cycles ********  48 h Fcsts**
**Standard dynamics and physics**
**Initialized with AWIP212 NCEP EDAS analyses, NAM forecasts for LBC’s**
**Uses MODIS composite SSTs**

**WRF -  **_**research**_** mode**
**Down to 2km resolution (Project Columbia) **
**Up to 51 vertical levels**
**ADAS for assimilation of AIRS profiles**
**LIS**

## Real-time MODIS Composite SST

**Real-time MODIS Composite SST**

**Lower boundary layer forcing important in mesoscale models**
**Current real time products used by modelers (e.g., the RTG SST analysis) lacks sufficient spatial resolution for regional models**
**Single pass of MODIS provides limited coverage/impact on forecast**

**Generate a simple composite generated from multiple passes of MODIS data (previous few days)****  **
** ****SST does not change significantly from **
**	****one day to the next**
** ****high resolution (1 km), full coverage**
** ****small scale features in SST gradients **
**       ****retained** ** **
**Expanding coverage area for SERVER and MAP06 applications**

## Validation of WRF forecasts (with MODIS SSTs)

**Validation of WRF forecasts (with MODIS SSTs)**

**GOES imagery provides validation for convective rolls**

**Small but significant differences due to MODIS SSTs produce better forecasts of surface fluxes/ convergence patterns over ocean**

## WRF Hurricane Forecasts w/SSTs

**Evaluate WRF simulations of tropical system **_**sensitivity to MODIS SSTs**_
** ****collaboration with GSFC**
** ****24 – 48 h forecasts at 2km resolution**
** ****Initialized with 40 km NAM analyses**
** ****NAM 3h forecasts used for LBCs**
*** ******parallel forecasts with either RTG SSTs***
***           ******or MODIS SST composite***

**42h forecast of**
**3h accumulated precip (in)**

**Hurricane Katrina**
**06 UTC August 29, 2005**

**WRF Hurricane Forecasts w/SSTs**

***Initial cases indicate that WRF hurricane intensity forecasts are sensitive to high resolution changes in SSTs.***

## SPoRT Research WRF with AIRS Data

_**WRF Configuration**_
**36km domain with 37 vertical levels**
**state-of-the-art dynamics and physics**
**initialized with NCEP 1****°**** GFS grids, with 6-h forecasts used as LBC**
_**ADAS Configuration**_
**vertical resolution to match WRF  **
**Bratseth SCM 3-D weights and AIRS error characteristics**
**selective use of AIRS T,q profiles based on quality indicators**
_**Assimilation / Forecast**_
**GFS interpolated to WRF **
**    ****domain, 1h forecast**
**WRF analysis at 07 UTC used as background for ADAS**

**1h forecast**

**WRF Domain for November 2005 Case Study**

## AIRS Data Overview

_**Prototype v5.0 AIRS retrievals much improved**_
_**Quality indicators by levels**_
**  ****pressure for each sounding indicating level**
**       ****of valid data**
**  ****error estimates for each profile at each **
**       ****level for T and q**
_**More data are assimilated**_
**  ****number of assimilated profiles reduced in **
**       ****v5.0, however**
** ****higher data volume –more data are used **
**      ****in the mid-troposphere (previously **
**      ****ambiguous QI here)**
**  ****assimilating a larger volume of higher     **
**      ****quality data produces an analysis that **
**      ****provides better initial conditions  **

**4.0 - black**
**5.0 - red**

## 29h Forecasts  -  Validation against RAOBs

_**Inclusion of v5.0 AIRS data**_**:**** **
**reduces bias in T and q forecasts at most levels**
**impact of data is greater – more data, greater coverage**
**spatial distribution of precipitation is improved**

***Prudent use of quality indicators with AIRS profiles can have a positive impact on short-term weather forecasts***

**AIRS v5.0          No AIRS           Stage IV**

**Cumulative Precipitation**