NASA/SPoRT Providing Real-time Numerical Weather Prediction Guidance for 2018 Winter Olympics

The NASA/SPoRT Center has developed a real-time numerical weather prediction (NWP) configuration that is being provided to forecasters in South Korea in support of the 2018 PyeongChang Olympics and Paralympic games.  The real-time modeling solution is part of a broader initiative known as the International Collaborative Experiment for the PyeongChang Olympics and Paralympic Winter 2018 Games (ICE-POP), which focuses on the measurement, physics, modeling, and prediction of heavy orographic snow in the PyeongChang Region of South Korea from January to March, 2018.  ICE-POP is led by the Korean Meteorological Administration (KMA) as a component of the World Meteorological Organization’s (WMO) World Weather Research Program (WWRP) Research and Development and Forecast Demonstration Projects (RDP/FDP).

The overarching ICE-POP goal is to gain a better understanding of orographic frozen precipitation processes, with the expectation that ICE-POP activities will also improve real-time weather forecasts and KMA-led decision support during the 2018 Winter Olympics. A coordinated array of surface, air and ship-borne meteorological instrumentation, radars, and NWP tools from numerous international partners (including NASA) support the ICE-POP objectives.  NASA’s participation in the ICE-POP RDP/FDP involves Marshall and Goddard Space Flight Centers collaborating as a team on a variety of common forecast and research goals.  The outcome of NASA’s involvement in ICE-POP will be the contribution of observational and modeling data that, as part of the larger ICE-POP dataset, will provide a more comprehensive understanding of orographic snowfall processes — a necessary step for improving and/or developing satellite-based snowfall retrieval algorithms and improved snow microphysics in NWP models.

For the real-time NWP solution as part of the ICE-POP FDP, SPoRT has configured the NASA Unified-Weather Research and Forecasting (NU-WRF) modeling framework to generate 24-hour forecasts four times per day, with initialization times at 0000, 0600, 1200, and 1800 UTC.  The model physics suite features the advanced 4-ice microphysics and short- and long-wave radiation parameterization schemes developed at Goddard Space Flight Center.  The NU-WRF grid setup consists of a triple-nested domain at 9-km, 3-km, and 1-km horizontal spacing, and 62 terrain-following vertical levels, covering regions spanning eastern Asia (9-km grid), the Korean peninsula and surrounding waters (3-km grid), and the eastern Korean peninsula centered on the Olympics venue (1-km grid; Fig. 1).  Initial and (lower) boundary conditions are provided by the NCEP Global Forecast System model and SPoRT’s own 2-km resolution sea surface temperature composite product.

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Figure 1. Depiction of the triple-nested grid configuration for the real-time NU-WRF forecast guidance, consisting of 9-km (upper-left), 3-km (right), and 1-km (lower-left) mesh grids.

Model fields are output every 3 hours on the 9-km grid, and every 30 minutes on the 3-km and 1-km grids.  The high-resolution output from the 1-km nest centered on the Olympics venue is being delivered in real time to South Korea forecasters for decision support during the games. SPoRT is sending full grids as well as point forecasts of model fields of interest at each specific game site.  Additionally, numerous graphics of temperature, moisture, winds, precipitation, snowfall, etc. are produced for each grid and hosted to a live model web page, accessible to the public.  The SPoRT/NU-WRF model output along with other models from participating international organizations will provide unique forecast guidance for advanced decision support during the Winter Olympics.  For more information and access to all the SPoRT modeling and remote-sensing products being served for ICE-POP, please link to the SPoRT ICE-POP project page.

Finally, an examination of the SPoRT/NU-WRF model guidance initialized at 1200 UTC 7 February offers a preview of anticipated conditions for the opening ceremony on 8 February.  A weak low pressure is forecast to move southeastward across the Yellow Sea, as indicated by the simulated mean sea level pressure and composite reflectivity from the 3-km grid in Figure 2.  However, this system should not impact the Korean peninsula, so the Olympic venues are forecast to remain free of precipitation.  Temperatures will be seasonably cold, as they are expected to remain below freezing at the venues for the next 24 hours (Fig. 3 animation of forecast 2-meter temperatures on the 1-km nested grid).  Visibility looks good, as it is forecast to remain above 10 km (Fig. 4 animation) with little to no low-level cloud cover being simulated by the 1200 UTC initialization of NU-WRF (not shown).  Enjoy the games and be sure to visit the SPoRT/NU-WRF modeling page often for short-term forecast weather conditions during the 2018 Winter Olympics!

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Figure 2.  Animation of 30-minute mean sea level pressure (hPa), 10-m winds (m/s), and composite reflectivity (dBZ) from the 3-km nested grid of the SPoRT/NU-WRF simulation initialized on 1200 UTC 7 Feb 2018.

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Figure 3.  Animation of 30-minute 2-m temperatures (deg C) and 10-m winds (m/s) from the 1-km nested grid of the SPoRT/NU-WRF simulation initialized on 1200 UTC 7 Feb 2018.

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Figure 4.  Animation of 30-minute surface visibility (km) and 10-m winds (m/s) from the 1-km nested grid of the SPoRT/NU-WRF simulation initialized on 1200 UTC 7 Feb 2018.

Lightning’s Reach

Over the weekend of October 22, thunderstorms moved through eastern Texas.  One feature that stood out was the number of flashes that extended well behind the main convective line of storms.  The image below shows where lightning was observed for one minute at 1258 UTC.  The overall Geostationary Lightning Mapper (GLM) event density display shows the lightning activity in a broken line from just west of Austin, Texas eastward towards Lake Charles, Louisiana.  Overall, the lightning remained fairly close to the parent storm.

The image is much more dramatic one minute later at 1259 UTC.  Here, GLM observes lightning extending from Austin, Texas all the way to Bryan and Waco, Texas and continuing northeast of Tyler, Texas.  Just from Bryan to Tyler, Texas this extends approximately 145 miles!

 

 

NOTE:  NOAA’s GOES-16 satellite has not been declared operational and its data are preliminary and undergoing testing. Users receiving these data through any dissemination means  (including, but not limited to, PDA and GRB) assume all risk related to their use of GOES-16 data and NOAA disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose.

 

Comparison of Soil Moisture Response in Hurricanes Harvey and Irma

Comparison of Soil Moisture Response in Hurricanes Harvey and Irma

After a record [nearly] 12 years between landfalling major hurricanes [cat 3 or higher], the United States has now experienced two major hurricanes making landfall less than three weeks apart from one another.  Hurricane Harvey brought exceptional record rainfall to southeastern Texas and southwestern Louisiana because it stalled shortly after landfall due to a lack of atmospheric steering currents.  Less than 3 weeks later, Major Hurricane Irma made landfall twice in Florida: once in the Lower Keys and again near Marco Island on the southwestern coast.  A long-lived cat 5 hurricane prior to landfall, Irma had a very large wind field which resulted in far-reaching impacts along the Florida East Coast, up to Charleston, SC, and inland to Atlanta, GA, with millions of households and businesses without electricity and/or water.

Here at the NASA SPoRT Center, we have been closely monitoring these two hurricanes through numerous social media and blog posts of unique satellite products and through SPoRT’s real-time instance of the NASA Land Information System (“SPoRT-LIS”).  This blog post serves to compare the soil moisture responses to hurricanes Irma and Harvey rainfall, as depicted by the real-time SPoRT-LIS output.  The Relative Soil Moisture (RSM) variable is shown throughout this article, since it takes into account the variations in soil composition by scaling the moisture availability between the wilting point (plants cannot uptake moisture) and saturation point (soil cannot hold any more water).  The SPoRT-LIS runs the Noah land surface model, which estimates soil moisture through 4 layers: 0-10, 10-40, 40-100, and 100-200 cm depth.  We first examine the response during Irma in the top 0-10 cm layer, followed by 0-100 cm layer for both storms, and then compare the total column (0-200 cm) values relative to historical values from a climatological database spanning 1981-2013 (33 years).

Figure 1 compares the weekly rainfall accumulation primarily from Hurricane Irma over the Southeastern U.S. to the August monthly rainfall total over Texas/Louisiana, primarily contributed from Hurricane Harvey during the final week of August. Rainfall from Irma was quite substantial in the Florida peninsula up to coastal South Carolina, where numerous locations measured over 10″ of rain in less than 2 days. Rainfall of 3-5″ extended inland to northern Georgia and central South Carolina, with lesser amounts generally below 3″ across eastern and northern Alabama (Fig 1, left panel).  The highest totals were along the southwestern and eastern Florida coasts.  This rainfall still pales in comparison to the widespread 20″+ that fell across a huge part of southeastern Texas and western Louisiana, albeit over a 5-6 day span.  Highest totals exceeded 50″ near Beaumont/Port Arthur, TX!

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Fig 1.  Comparison of weekly rainfall estimate associated with Hurricane Irma (left), and August monthly rainfall estimate associated with Hurricane Harvey (right).

The 0-10 cm RSM animation in Fig 2 for hurricane Irma shows how quickly the top soil layer responds to incoming rainfall within the Noah land surface model in SPoRT-LIS.  The heavy rainfall rates up to 4″ per hour or more led to a quick saturation during 10 September across the Florida peninsula, eventually extending up to coastal Georgia and South Carolina on the 11th.  Similarly, as rainfall ends we can see the 0-10 cm RSM quickly decrease from south to north as the moisture infiltrates into deeper model layers and/or evaporates back to the atmosphere.  We also see that the top soil layer does not completely saturate across interior Georgia and Alabama, likely due to lower rain rates, drier initial soils, and different soil composition compared to the fast-responding sandy soils across Florida.

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Fig 2.  Hourly animation of SPoRT-LIS 0-10 cm relative soil moisture (RSM) and Multi Radar Multi Sensor (MRMS) quantitative precipitation estimates (QPE) from 0000 UTC 10 September through 1200 UTC 12 September 2017, associated with Hurricane Irma.

Meanwhile, the RSM averaged over the top 3 layers (0-100 cm; Fig 3) takes a longer time to moisten up during the heavy rainfall of Irma. We do see values approaching saturation across southwestern, central, and particularly northeastern Florida near the end of the rainfall event as the deeper soils have had an opportunity to recharge.

Over southeastern Texas and Louisiana (Fig 4), the 0-100 cm RSM animation shows how the prolonged, training heavy rainfall led to near saturation of the top meter of the Noah model, despite dry antecedent conditions (especially west of the Houston metro, where the RSM transitioned from less than 10% to nearly saturation!).  The much longer rainfall duration with hurricane Harvey led to sustained higher values of soil moisture in the top one meter.

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Fig 3.  Hourly animation of SPoRT-LIS 0-100 cm RSM and MRMS QPE from 10-12 September 2017, associated with Hurricane Irma.

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Fig 4.  Hourly animation of SPoRT-LIS 0-100 cm RSM and MRMS QPE from 25-30 August 2017, associated with Hurricane Harvey.

Finally, the total column 0-200 cm layer can require months or years to respond to rainfall events (or lack thereof), depending on the soil composition.  However, with major rainfall events like hurricanes Harvey and Irma, the total column RSM does respond dramatically and subsequently can depict substantial wet anomalies.  To that end, the SPoRT-LIS has a daily, county-based climatological database of modeled soil moisture from 1981-2013 from which current conditions can be compared to depict anomalies via percentiles relative to the 33-year distribution.  Fig 5 shows these percentiles color-coded to depict dry anomalies (less then 30th percentile) or wet anomalies (greater than 70th percentile) according to the scales beneath the figure.

Following hurricane Irma, we see that portions of southwestern and northeastern Florida have 0-200 cm RSM greater than the 98th percentile, as well as parts of west-central Georgia (Fig 5; left panel).  In general, the extreme wet percentiles are fairly spotty across the domain.  However, following hurricane Harvey (Fig 5; right panel), the 0-200 cm RSM percentiles are “off the charts” high, with dozens of counties experiencing soil moisture exceeding the [33-year] historical 98th percentile.  In fact, the soil moisture was SO anomalously moist following hurricane Harvey that the average daily value across all of Jefferson County, TX (Beaumont/Port Arthur) exceeded all values in the entire 33-year database by the end of August!  This unusual condition is highlighted in Fig 6, which shows a daily animation of historical 0-200 cm RSM histograms for Jefferson County, TX, with the current 2017 county-averaged values in the vertical dashed line.  We see that by the end of hurricane Harvey, the vertical dashed line is well above any values from the 33-year historical distribution, thereby quantifying how exceptionally unusual this rainfall event was in southeastern Texas.

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Fig 5.  SPoRT-LIS 0-200 cm RSM percentile, valid at 1200 UTC on 12 September 2017 (post-Irma; left), and 30 August 2017 (post-Harvey; right).

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Fig 6. Animation of daily distributions of 0-200 cm RSM for all SPoRT-LIS grid points residing in Jefferson County, TX (Beaumont/Port Arthur) during the month of August 2017.  Gray bars are the frequencies of 0-200 cm RSM from the 33-year SPoRT-LIS climatology; colored vertical lines are reference percentiles according to the legend in the upper right; and the bold vertical dashed line is the county-averaged value for the present day in August 2017.

Passive Microwave Views of Three Atlantic Hurricanes This Morning…

Below are 89 GHz RGBs (composited) of the three hurricanes affecting the Atlantic basin this morning.  Notice a decent eye structure is observable in all of the storms, including Hurricane Katia in the SW Gulf of Mexico.  This was noted in the 4 AM CDT discussion about the hurricane from the National Hurricane Center (NHC), “Enhanced BD-curve infrared imagery and a GPM microwave composite image indicate improved banding over the western portion of the circulation and the earlier ragged eye presentation has become much more distinct.”  SPoRT helped with the implementation of the passive microwave data into the AWIPS platform at the NHC several years ago, which has aided forecasters there with the diagnosis and analysis of these systems.

For the latest, best up-to-date information regarding the hurricanes, please refer to the NHC website.

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89 GHz RGBs from the GPM constellation of the three hurricanes affecting the Atlantic Basin this morning.  Approximate times for passes over the respective hurricanes are noted in the image.

Passive Microwave Observations of Category 5 Hurricane Irma…

The NASA SPoRT program has been providing Passive Microwave data to the National Hurricane Center for use in the NWS Automated Weather Interactive Processing System (AWIPS), which allows for data-layering capabilities, and has helped improve the method for tracking center fixes on tropical systems.  These data have been provided to the NHC as part of an on-going collaborative effort for several years now.  These first two images are 89 GHz RGBs taken over Cat-5 Irma from this morning.  Importantly, these data/imagery help forecasters to better analyze the internal hydrometeorological structure of tropical systems over other traditional satellite imagery.

Image 1.  89 GHz image over Cat-5 Hurricane Irma from approx 0548 UTC 7 Sep 2017.  Background imagery is SPoRT SSTs from approx 18 UTC 6 Sep 2017.

 

Image 2.  89 GHz image over Cat-5 Hurricane Irma from approx 1112 UTC 7 Sep 2017.  Background image is SPoRT SSTs from approx 18 UTC 6 Sep 2017.

Sea surface temperatures to the west of the system, and ultimately where it will be tracking are on the order of about 85-87 degrees F, according to the SPoRT data.  The warmest waters are found generally in the SW portions of the Bahamas.

Lastly, here are the 89 GHz Horizontal and Vertical data/imagery for each of these times, that comprise the RGB.

Image 3. 89 GHz Horizontal image over Cat-5 Hurricane Irma from approx 0548 UTC  (left) and 1112 UTC (right) 7 Sep 2017.

Image 4. 89 GHz Vertical image over Cat-5 Hurricane Irma from approx 0548 UTC (left) and 1112 UTC (right) 7 Sep 2017.

 

Category 5 Hurricane #Irma As Observed by the #GOES16 #GLM

Below is a sample image from the linked mp4 movie of hurricane Irma at 1501 UTC immediately afer the National Hurricane Center upgraded the intensity to 180 mile per hour sustained winds.

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Hurricane Irma at 1501 UTC just after the National Hurricane Center upgraded the intensity to 180 miles per hour.  This figure shows the ABI 10.3 micron infrared imagery with the corresponding GLM 8 km event density (to show spatial extent) and flash centroid points.  In this image 10 GLM flashes were observed.

This morning the National Hurricane Center upgraded hurricane Irma to a category 5 storm (7 AM Central, 1200 UTC).  By 10 AM Central (1500 UTC), the hurricane further intensified with maximum sustained winds of 180 miles per hour.  The linked mp4 movie covers just over 3 hours from ~1150 UTC through 1510 UTC.  The animation shows the ABI 10.3 micron infrared imagery (full disk, 15 minute resolution) and the GLM event density (to show spatial extent) and the flash centoid points.  This display is in AWIPS and uses the combined event density and flash centroid plot recommended by the Hazardous Weather Testbed (HWT) along with the color curve developed by NASA SPoRT and tested at HWT.  Of note is to watch the location of lightning in the storm as it varies between the outer bands and the eyewall.

[29 MB] Hurricane Irma mp4 link

NOTE:  NOAA’s GOES-16 satellite has not been declared operational and its data are preliminary and undergoing testing. Users receiving these data through any dissemination means  (including, but not limited to, PDA and GRB) assume all risk related to their use of GOES-16 data and NOAA disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose.

Monitoring Hurricane Harvey with the Geostationary Lightning Mapper (GLM)

Figure 1, below, shows a single image from the attached movie showing the Geostationary Lightning Mapper observations for Hurricane Harvey.

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Figure 1:  Example image from the attached movie from 0700 UTC on August 25, 2017 while Harvey is a Category 2 hurricane.  The image shows the 11.2 micron infrared imagery in grey scale (background) and the GLM group density (shaded) accumulated over 15 minutes.

The link to the animation below monitors Harvey from 1400 UTC on August 23, 2017 while it was still a remnant system coming off the Yucatan Peninsula through the initial landfall and heavy precipitation across Texas at 2345 UTC on August 27, 2017.  The animation shows the ABI 11.2 micron infrared imagery (using a greyscale color curve to emphasize GLM) as well as the GLM 8 km group density (using the SPoRT color curve tested at the Hazardous Weather Testbed and being prepared as the default, operational curve).  Given the length of time covered by the animation, the data are shown at 15 minute intervals.  Specifics on Harvey (i.e., maximum winds and minimum pressure) are from the National Hurricane Center’s product archive for this storm.  The link is for an mp4 movie and is approximately 62 MB in size.

[62 MB]  Hurricane Harvey mp4 link.

Several still images are shown below highlighting interesting features.

One feature is the distribution of the total lightning observations throughout the tropical cyclone and the magnitude of the lightning density.  Generally, the total lightning is not distributed throughout the entire storm, but concentrated in bands and sometimes in the eye wall, as seen in Figure 1.  Figure 1 can be compared to the early stages when Harvey was upgraded to a Tropical Storm (Figure 2), but also later where many times there is no lightning in the eye wall (Figure 3). Also, please note that the accumulations are for 15 minutes versus 1 or 2 minutes shown in severe weather cases.

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Figure 2:  This is the same as Figure 1, but for Tropical Storm Harvey at 0415 UTC on August 24, 2017.  

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Figure 3:  This is the same as Figure 1, but at 0815 UTC on August 25, 2017.  This is highlighting the distribution of total lightning is mainly in the convective bands and note in the eye wall.

Figure 4 shows Harvey as it makes landfall as a Category 4 hurricane.  Here, GLM group density values are on par with the outer convective bands as the eye wall makes landfall.

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Figure 4:  Same as Figure 1, but at 0215 UTC on August 26, 2017.  This image highlights the GLM group density observations in Hurricane Harvey’s eye wall as it makes landfall.

Lastly, after the initial landfall, Figure 5 shows a large increase in the magnitude and spatial area of the GLM group densities in the outer convective band as some of the catastrophic rain impacts the Houston, Texas region.

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Figure 5:  Same as Figure 1, but at 0245 UTC on August 27, 2017.  This image highlights the increased GLM group density magnitude and spatial extent during part of the catastrophic rains that impacted Houston, Texas.

NOTE:  NOAA’s GOES-16 satellite has not been declared operational and its data are preliminary and undergoing testing. Users receiving these data through any dissemination means  (including, but not limited to, PDA and GRB) assume all risk related to their use of GOES-16 data and NOAA disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose.