A Decade Review of SPoRT

A Decade Review of SPoRT

Written by Emily Berndt and Jordan Bell

SPoRT was established in 2002 to transition NASA satellite data and capabilities to improve short-term weather forecasting with an emphasis on National Weather Service (NWS) end users. With the goal of maximizing the benefit of NASA research and capabilities to benefit society, SPoRT has developed innovative solutions to bring research products to operations and tailor them to meet end user needs. Over the past decade SPoRT has been at the forefront of a range of activities, making notable contributions to NASA LIS and WRF Hydro, the GOES-R/JPSS Proving Grounds, and the GPM, SMAP, and SWOT Early Adopter Programs. With an initial focus on partners in the southeastern U.S., SPoRT has expanded partnerships to include end users in all NWS Regions, National Centers, and other government agencies such as the U.S. Forest Service, U.S.D.A., and state environmental agencies. Over the decade SPoRT has consistently used a research to operations/operations to research paradigm to interact with end users, involving them in the process of product development, tailored training, and product assessment/feedback. This process has even led to algorithm improvements within GPM IMERG and the NESDIS Snowfall Rate to accelerate operational use of research products.  Interaction with end users has even led to the pursuit of research projects such as limb correction to improve RGB imagery and interpretation or developing a methodology to correct land surface model data with satellite soil moisture. In order to introduce experimental products into the fast-paced operational environment SPoRT developed applications-based training concepts such as the Quick Guide that has been shared with and adopted by others in the community. Also notable- early activities within SPoRT to leverage NASA data for disaster response, led to a bigger presence in and significant contributions to the NASA Disasters Program. Below is a review of notable publications, blog posts and tweets over the past decade:

— 2010 —

Notable Publication

Utilizing Total Lightning Information to Diagnose Convective Trends

Top Blog Post

Experimental MODIS RGB Color Composites of Hurricane Earl

— 2011 —

Notable Publication

NASA satellite data assist in tornado damage assessments

Top Blog Post

Analyzing MODIS Imagery of North Alabama Tornado Tracks

Top Tweets

— 2012 —

Notable Publications

The GOES-R Proving Ground: Accelerating User Readiness for the Next-Generation Geostationary Environmental Satellite System

Diagnosis of a dense fog event using MODIS and high resolution GOES satellite products with direct model output

Top Blog Post

Dust Storm in the Plains Captured well in MODIS Dust RGB Imagery

Top Tweets

— 2013 —

Notable Publications

Transitioning research satellite data to the operational weather community: The SPoRT Paradigm

Transitioning research to operations: Transforming the “valley of death” into a “valley of opportunity

The emergence of weather-related test beds linking research and forecasting operations

The GOES-R Geostationary Lightning Mapper (GLM) 

Application of next-generation satellite data to a high-resolution, real-time land surface model

Multispectral imagery for detecting stratospheric air intrusions associated with mid-latitude cyclones

Top Blog Post

Long flash observed by the Colorado Lightning Mapping Array

Top Tweets

— 2014 —

Notable Publications

A Real-Time MODIS Vegetation Product for Land Surface and Numerical Weather Prediction Models 

Total lightning observations and tools for the 20 May 2013 Moore, Oklahoma, tornadic supercell

Satellite-based identification of tornado damage tracks from the 27 April 2011 severe weather outbreak 

Top Blog Post

VIIRS Day Night Band (DNB) RGB Imagery Assisted by Nighttime-Microphysics RGB

Top Tweets

— 2015 —

Notable Publications

Development and Application of Atmospheric Infrared Sounder Ozone Retrieval Products for Operational Meteorology

Satellite tools to monitor and predict Hurricane Sandy (2012): Current and emerging products

Transitioning NASA and NOAA Satellite Products, Modeling & Data Assimilation Techniques, and Nowcasting Tools to Operations

Demonstration of a GOES-R Satellite Convective Toolkit to “Bridge the Gap” between Severe Weather Watches and Warnings: An Example from the 20 May 2013 Moore, Oklahoma, Tornado Outbreak

Top Blog Post

From Drought To Flooding In Less Than A Week Over The Carolinas As Depicted By SPoRT LIS

Top Tweets

— 2016 —

Notable Publications

Assimilation of SMOS Retrievals in the Land Information System

Limb correction of MODIS and VIIRS infrared channels for the improved interpretation of RGB composites

Next Generation Satellite RGB Dust Imagery Demonstration Leads to Changes in Communication and Services by NWS Albuquerque Forecast Office

From drought to flash flooding in less than a week over South Carolina

The operational use and assessment of a layered precipitable water product for weather forecasting

Monitoring and tracking the trans-Pacific transport of aerosols using multi-satellite aerosol optical depth composites

Top Blog Post

Precip Estimates offshore using NASA IMERG

Top Tweets

— 2017 —

Notable Publications

Transforming satellite data into weather forecasts

Lightning decision support using VHF total lightning mapping and NLDN cloud-to-ground data in North Alabama 

Top Blog Post

Category 5 Hurricane Irma as Observed by the GOES 16 GLM

irma_cat5_1minGLM_05sep17-190

 Top Tweets

— 2018 —

Notable Publications

A Methodology to Determine Recipe Adjustments for Multispectral Composites Derived from Next-Generation Advanced Satellite Imagers

Utility of CrIS/ATMS profiles to diagnose extratropical transition

Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation

Evolution of 2016 drought in the southeastern United States from a land surface modeling perspective

Snowfall rates from satellite data help weather forecasters

Impact of dust aerosols on precipitation associated with atmospheric rivers using WRF-Chem simulations

Characteristics of Lightning Within Electrified Snowfall Events Using Lightning Mapping Arrays 

Top Blog Post

Plenty of fresh Powder for Paralympic Winter Games in-Pyeongchang Three Snowstorms in Eight Days

Top Tweets

— 2019 —

Notable Publications

Incorporation and Use of Earth Remote Sensing Imagery within the NOAA/NWS Damage Assessment Toolkit

Geostationary Lightning Mapper Flash Characteristics of Electrified Snowfall Events

Limb Correction of Geostationary Infrared Imagery in Clear and Cloudy Regions to Improve Interpretation of RGB Composites for Real-Time Applications

Addressing the Cold Air Aloft Aviation Challenge with Satellite Sounding Observations

Gulf of Alaska cyclone in daytime microphysics RGB imagery

Development and Evaluation of the GLM Stoplight Product for Lightning Safety

Spatial, Temporal and Electrical Characteristics of Lightning in Reported Lightning-Initiated Wildfire Events 

Top Blog Posts

GLM Sees Apparent Meteor Flash in Western Cuba

Normalized Burn Ratio (NBR) Imagery in AWIPS

Top Tweets

Into the next decade

During the past decade SPoRT has made notable contributions to bridge the valley of death to transition research to operations and maximize the benefit of NASA and NOAA remote sensing observations for the benefit of society.  SPoRT has conducted a range of research in key areas including modeling and satellite data assimilation, remote sensing, and lightning.  In addition, SPoRT has partnered with other researchers, product/algorithm developers, and end users to assess products in the operational environment, create training, and assess their utility.  The team has observed research capabilities transform into operational products as a result of end user interaction and many of those examples are highlighted above! Into the next decade SPoRT will continue to foster interaction between research and operations as well as conduct research in focus areas that include lighting, synoptic/mesoscale meteorology, tropical meteorology, land surface modeling, health/air quality, and hazards.  SPoRT has already begun engaging in new NASA missions such as TEMPO and TROPICS that will bring unprecedented observations to benefit science and applications.  In addition, SPoRT is using their expertise in transition of research to operations to anticipate applications of future missions by actively participating in the NASA Decadal Survey Designated Observable studies.  We look forward to continuing to bridge the gap between research and operations, bringing new NASA capabilities to end users, in the new decade ahead! Thank you to all the SPoRT team members,  collaborators, and end users who have contributed to many of the projects described above.

Transition to CONUS SPoRT LIS Underway…

So, we’ve finally begun the process of transitioning over fully to the new CONUS version of the SPoRT LIS.  This “new” version of the SPoRT LIS has been under development actually for several years now, and underwent initial testing and evaluation at the Huntsville WFO in spring 2015, followed by an evaluation by several WFOs and RFCs in summer 2015.  Image 1 below shows the differences in the domains.  The new version of the SPoRT LIS encompasses the entire CONUS and surrounding areas of southern Canada and northern Mexico, albeit with some anticipated degradation especially in the border regions due to lack of consistent radar/precipitation coverage.

lispostimage1_28sep2016

Image 1. The CONUS SPoRT LIS (left) and the approximate domain of the old Southeast CONUS version (right).  Note: the images are from different periods.

Not only does the CONUS version offer a geographic expansion over the previous version of the LIS, but new variables are a part of the new SPoRT LIS, including 0-200 cm relative soil moisture changes on several timescales (weekly, bi-weekly, monthly, seasonal, semi-annual and annual) soil moisture percentiles and soil temperatures.  The soil moisture percentiles and change values can be especially useful for the drought designation and analysis process, and have been used in this capacity at the Huntsville office since their inception.  Of course, there are other applications for hydrology, fire weather and blowing dust.  We’re planning to explore more of these latter unique and interesting applications with several of SPoRT’s collaborative Western CONUS WFOs next spring and summer.  The SPoRT LIS soil temperature data have shown promising application for impacts during winter weather events during evaluation of a few events in the previous winter, with more evaluation expected during the upcoming winter.  In addition to the new variables, the new version of the SPoRT LIS is using NSSL’s Multi-Radar Multi-Sensor data for precipitation forcing in the near term and is also solely incorporating the VIIRS GVF over the legacy MODIS GVF.

lispostimage2_28sep2016

Image 2. Examples of SPoRT LIS 0-200 cm relative soil moisture weekly change (left) and 0-200 cm relative soil moisture percentile (right)

Users of the SPoRT LIS and GVF data for their local modeling purposes will need to make the appropriate changes to their EMS/UEMS model runs to properly incorporate these new data sets.  Please contact Jon Case at SPoRT or me (Kris White) if you have any questions.  Thanks for reading!

SFR performance during transition from rain to wintry precipitation.

SFR_0220UTC_02162016

During the afternoon and early evening hours on 2/15/2016, a large area of rain covered much of northeast Kentucky and southeast Ohio as well as the western half of West Virginia.

An upper level disturbance then moved across the area during the evening and overnight hours with the rainfall mixing with and then transitioning to all snow.

I wanted to show how the SFR image performed during this transition.  The image above is from 0220 UTC on 2/16/2016.  At that time, much of the precipitation across West Virginia was still in the form of rain…with an area of snow extending from northwest Pennsylvania across central Ohio into southwest portions of that state.

There appears to be several observations of rain across Ohio with surface temperatures  of 32 to 35 DegF  where the SFR product indicated snow in the clouds.  It does appear that where surface temperatures were warmer than 35 DegF, the SFR product did not indicate any snow in the clouds.

From an earlier post, I believe the SFR throws out snow when the model-based 10-m temperatures exceeded 33 DegF.  Is this filter working in this situation?

 

SFR performance at temperatures below 22 DegF

SFR_1605UTC_012614

On Jan 26 2014, an upper level shortwave caused an area of light snow across Ohio, western Pennsylvania and the northern counties of West Virginia. Surface temperatures were quite cold with readings generally in the teens. Even at these cold temperatures, the SFR product did indicate snowfall across the far northern counties of our forecast area.

The maximum snowfall rates indicated on the 1605 UTC product was about 0.3 to 0.4 inches per hour. Based on reports, these numbers appear to be representative of what actually was occurring.

While this is just one case, the SFR product appears to work reasonably well at temperatures below 22 DegF.

Snow Advisory Event on Jan 25 2014

Radar_1106UTC_012514SFR_1120UTC_012514

On Jan 25 2014, a mid-level shortwave moved across the region generating light to moderate snow. I have included screen captures of the 1118 UTC regional radar mosaic and surface observations…along with a 1120 UTC Snowfall Rate Product and surface observations.

It looks like the SFR product did not detect all of the snow that was falling around 11 UTC. But the misses can generally be described as either (1) the surface temperatures being too cold or (2) the probabilistic model, that is part of the calulations, indicating probabilities that were too low to determine if there was snow.

Once you know all of the details on how the product is calculated, I think this product did a good job at detailing where the snowfall was occurring.

The highest snowfall rates indicated by this image was around 0.3 to 0.5 inches which seems to be representative of what was occurring.

SFR product not showing snow where radar and surface observations indicated light to moderate snow was falling.

SFR_1522UTC_012114

When I examined the 1522 UTC SFR product, I noticed there was an absence of snow across our forecast area. Radar and surface observations indicated that light to moderate snow was continuing across most of our counties.

Per the Quick Guide, I checked the surface observations to see if the temperatures were about 22 DegF or colder. The temperatures across our northern and western counties were actually 22 DegF or colder. So the SFR product was behaving as it should across those counties.

However, the temperatures across the remainder of our region were above 22 DegF. The snow is definitely not lake effect as the current snow was still related to a shortwave which had pushed to our east.

What could be causing the lack of indicated snow across the portions of our area that still had surface temnperatures above 22 DegF?

SFR product performance during snow across WFO RLX’s forecast area.

All,

I have attached a screen capture of the SFR product from 1024 UTC on 1/21/14.  The label on the image is wrong.  It states the units of the product are in/hr.  But they are actually mm/hr.

During this time, we were having widespread light to moderate snow as an upper level disturbance moved across our forecast area.  Reports around 2 inches of snow were common around the time of the product.   We had received reports of snow coming down around an inch per hour.  The maximum SFR detected in the product was 1.6 mm/hr…or 0.06 in/hr.  Using a ratio of 15:1 yields a maximum snowfall rate around 0.9 inches per hour.

While we had several surface observations from which we could estimate precipitation rates, our WSR-88D was not operating correctly.  The legacy precipitation were okay.  But the Dual Pol precipitation products were not totally reliable due to equipment issues.  So the additional information from the SFR product should have helped estimate the precipitation rates.

SFR_1024Z_012114