Extreme Wildfire Setup over Southern High Plains for 17 April

The fire weather outlook for today (17 April 2018) looks very dire over the Southern High Plains of western Texas, New Mexico, and portions of western Oklahoma, southwestern Kansas, and southeastern Colorado. The combination of very little precipitation in recent months along with expected strong winds and extremely low relative humidities will set the stage for potentially dangerous wildfires over this region. The NCEP Storm Prediction Center has the highest threat category in today’s fire weather outlook across the region, with a large swath of extremely critical fire weather conditions expected (Fig. 1).

The persistent lack of precipitation over the Southern High Plains and Desert Southwest regions and its impact on deep-layer soil moisture is captured by the SPoRT-LIS 6-month change in total column relative soil moisture, as posted on the SPoRT-LIS graphics web page (Fig. 2; https://weather.msfc.nasa.gov/sport/case_studies/lis_CONUS.html).  A sharp transition lies across Kansas, Oklahoma and Texas, where a strong drying signal is seen across western portions of these states into New Mexico, Arizona, and Mexico, whereas dramatic moistening is prevalent in the last 6 months over the Mississippi, Ohio, and Tennessee River Valleys.  Substantial drying is also noted over the southern Florida Peninsula, with wetting seen over the West Coast and Pacific Northwest (Fig. 2).

Since the unusual and persistent dry pattern over the Southern Plains and Desert Southwest has occurred during the winter months when vegetation is typically dormant (which taps into the deeper soil moisture layers), the anomalously dry conditions are best captured by soil moisture percentiles in the near-surface layer of the SPoRT-LIS.  The total column SPoRT-LIS soil moisture percentiles does not depict an overly dramatic anomaly over the Desert Southwest (Fig. 3; unusual dryness is most prevalent in the deep layers from Oklahoma/Kansas up to Wisconsin/Illinois); however, the shallow soil moisture percentiles capture the anomalous drying over these regions near the surface, as seen in the animation of daily 0-10 cm percentiles for April  in Fig. 4, especially over West Texas, New Mexico and Arizona.  Medium-range forecasts suggest there could be precipitation over the Southern High Plains this weekend, but numerous wetting events will be needed to relieve the ongoing drought conditions.

Figure 1. NCEP Storm Prediction Center’s Day-1 fire weather outlook map for 17 April 2018.

Figure 2. Six-month change in SPoRT-LIS total column relative soil moisture for the period ending 16 April 2018.

Figure 3. SPoRT-LIS total column relative soil moisture percentiles, valid for 16 April 2018.

Figure 4. Daily animation of top-layer (0-10 cm) SPoRT-LIS soil moisture percentiles for the period 1 April to 16 April 2018.

Dust RGB Imagery and NASA’s CALIPSO/CALIOP

Dust RGB Imagery and NASA’s CALIPSO/CALIOP

anigif850x484_DustRGB_Southwest_20180412_1732_2122

Dust RGB via GOES-16 from 1732-2232 UTC on 12 April 2018 over the U.S. Southwest

SPoRT used the MODIS and VIIRS imagers (on NASA’s Aqua & Terra satellites, and NOAA’s S-NPP satellite, respectively) within the NOAA’s Satellite Proving Ground to assess the value of a “Dust RGB Imagery” product for potential use with GOES-R (now GOES-16).  The Dust RGB proved useful on 13 April 2018 (animation above) where many dust plumes developed in the U.S. Southwest and Mexico.  Forecasters were able to monitor dust plume initiation and issue advisories and warnings.  In addition, several plumes continued to have impacts after sunset, and the Dust RGB, which uses only IR window channels (see Dust RGB Quick Guide), was able to continue monitoring the event at night while the visible imagery was no longer valuable.  Some advisories were extended beyond their original expiration time.  NASA SPoRT is using the NASA CALIPSO satellite and associated CALIOP lidar on board to validate and categorize dust signatures seen in the RGB and examine quantitative aspects like plume height and thickness.  The image below shows an event from 3 April 2018 where forecasters from the NWS Albuquerque WFO and CWSU evaluated the Dust RGB impact to operations as part of SPoRT’s assessment activities, and the CALIOP lidar backscatter captured the dust plume over west Texas.  From CALIOP the dust plume appears to be about 2 km thick in most locations, but the most concentrated region reached a height of about 3 km above ground.

DustRGB_CALIOPbackscat_20180403

Dust RGB via GOES-16 (upper) over the CONUS and lidar backscatter via CALIOP (on NASA’s CALIPSO satellite) for 2007 UTC on 3 April 2018.  Annotations in yellow point out the dust plume and clouds along the path of CALIOP shown by white arrow/text.

Assimilation of NASA Soil Moisture Active Passive (SMAP) Retrievals to Improve Modeled Soil Moisture Estimates and Short-term Forecasts

For several years, SPoRT has been running a real-time simulation of the NASA Land Information System (hereafter, “SPoRT-LIS“), over a Continental U.S. domain at a ~3-km spatial resolution.  The SPoRT-LIS product is a Noah land surface model climatological and real-time simulation over 4 model soil layers (0-10, 10-40, 40-100, and 100-200 cm). For real-time output, the Noah simulation is updated four times per day as an extension of the long-term climatology simulation.  It ingests NOAA/NESDIS daily global VIIRS Green Vegetation Fraction data, and the real-time SPoRT-LIS component also incorporates quantitative precipitation estimates (QPE) from the Multi-Radar Multi-Sensor (MRMS) gauge-corrected radar product.  The long-term, climatological SPoRT-LIS is based exclusively on atmospheric analysis input from the NOAA/NASA North American Land Data Assimilation System – version 2.

Over the last 1-2 years, SPoRT has been conducting applied research to improve its SPoRT-LIS analyses by assimilating Level 2, enhanced-resolution soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission.  Comparisons between the “SMAP-LIS” (assimilating the SMAP soil moisture retrievals) and the current SPoRT-LIS (without data assimilation) are available in a near real-time research page.  Data assimilation experiments are being conducted over both Continental U.S. and East Africa domains, and the utility of SMAP data assimilation over Alaska during the warm season is being explored as well.  Besides producing more accurate soil moisture estimates, the project also seeks to improve short-term numerical weather prediction (NWP) models by comparing model runs initialized with SPoRT-LIS enhanced with SMAP data assimilation against model runs initialized with the current SPoRT-LIS soil moisture fields.  This blog post provides a status of this ongoing research, highlighting recently-published work and preliminary NWP model results.

Improvements to Modeled Soil Moisture Estimates

[The data assimilation discussion below represents an excerpt from Blankenship et al. 2018]

To reduce model forecast error in a land surface or atmospheric model, it is essential to periodically update model states with independent observations (Fig. 1).  Data assimilation methods are used to combine an existing model state (background) with a set of observations in order to produce a new model analysis.  The SMAP-LIS uses an Ensemble Kalman Filter to combine SMAP observations with the modeling capabilities of the previous SPoRT-LIS, with the goal of improving states of soil moisture , soil temperature, and fluxes of moisture and energy at the land surface.

Fig1_DA-concept

Figure 1.  Conceptual diagram of data assimilation (DA).  The black curve represents the true state of some model variable over time at a single point.  The red curve represents a forecast unconstrained by observations and whose error grows with time.  With data assimilation, the observations (blue diamonds) are used to adjust the model value at each assimilation cycle (gray arrows), producing a new forecast (purple curve) that is closer to reality.

An important step in data assimilation is to perform a bias correction to adjust the observations to match a known model distribution.  This is desirable because the solution for the new model analysis makes the assumption that the model and observation are unbiased relative to each other.  The choice of the temporal and spatial scales for this correction are somewhat subjective.  If the initial model climatology has biases built in, e.g., a systematic wet bias or a regionally-varying bias, a strict correction to the model climatology will maintain the model’s previous bias.  Since we seek to take advantage of the global consistency of SMAP observations, we have implemented a non-local bias correction by aggregating points to generate a location-independent correction curve for each soil type (since many modeling errors are related to soil type).  Figure 2 shows the resulting correction curves for 8 broad soil-type categories.

An advantage of applying the weaker non-local constraint when performing the bias correction is that it allows SMAP to influence the climatology of the soil moisture.  We have identified some geographic model biases in our existing SPoRT LIS run, forced by NLDAS-2 analyses.  One example involves the blending of disparate US and Canadian precipitation observations in the Great Lakes region.  This blending produced a persistent dry anomaly in the southern Ontario region (between Lakes Superior, Erie, and Ontario) in the SPoRT-LIS.  This is seen in Fig. 3a, which shows the monthly average 0-100 cm soil moisture for June 2016.  The SMAP retrievals (A single overpass is shown in Fig. 3c.), while relatively dry in this region, are more consistent with nearby areas in Michigan and the northern parts of Indiana and Ohio.  (Note that the color scale is different since this figure represents the top 5 cm only.)  As the result of repeated data assimilation, the SMAP-LIS soil moistures (Fig. 3b) in Southern Ontario over the 0-100 cm layer are more consistent with those in neighboring regions to the south and west.  This example illustrates how the non-local bias correction can help correct spatially varying errors in the model soil moisture.

A quantitative validation (Table 1) was performed against a soil moisture gauge at Elora, Ontario (depicted by the star in Fig. 3c) for two summers.  Results show that the SMAP-LIS soil moistures were more accurate in terms of bias and RMSE for 2015 and 2016.  Results for unbiased RMSE, correlations, and anomaly correlations were mixed from year to year but all three metrics performed better in the second year of the experiment.

Fig2_SM-bias-correction

Figure 2. Bias correction curves for 8 soil type groupings used to convert SMAP retrievals to model-equivalent values.

Fig3_SMAPDA-differences-SEcanada

Figure 3. Long-term impact of SMAP data assimilation on root zone soil moisture. (a) Average of 1200 UTC 0-100 cm relative soil moisture (%) for June 2016 from SPoRT-LIS (no data assimilation), (b) Same quantity but for SMAP-LIS, (c) SMAP retrieved soil moisture on 4 June 2016. (m3 m-3 x100).  The star shows the location of a soil moisture gauge used for validation. (Click on image for full size view)

TABLE 1. Validation statistics (bias, RMSE, unbiased RMSE, correlation, anomaly correlation) from Elora, Ontario, Canada soil moisture gauge for summer 2015 (30 May-4 Sep) and summer 2016 (2 May-31 Aug).  For each pair of measurements, the better value is in bold type.

2015

2016

Metric SPoRT-LIS SMAP DA SPoRT-LIS SMAP DA
Bias -0.096 -0.077 -0.083 -0.043
RMSE 0.102 0.088 0.115 0.086
ubRMSE 0.036 0.042 0.079 0.075
RCORR 0.76 0.69 0.38 0.48
ACORR 0.77 0.67 0.55 0.57

Impacts on Short-term NWP Model Forecasts

The second component of this research involves compared NWP model simulations initialized with SPoRT-LIS and SMAP-Enhanced DA fields, using the NASA Unified-Weather Research and Forecasting (NU-WRF) modeling framework for the experiments.  NU-WRF simulations are currently focused on the CONUS during the warm season (May to August) to document impacts of SMAP data assimilation on short-term regional NWP.  A case study of improved timing of a mesoscale convective system (MCS) is highlighted here.  Ongoing work involves examining other high-impact convective cases during the 2015 and 2016 warm seasons, and conducting comprehensive model verification statistics.

The case highlighted here is from a severe MCS over the Midwest from 13-14 July 2016.  The NCEP/Storm Prediction Center reports from this day are available at http://www.spc.noaa.gov/climo/reports/160713_rpts.html. An MCS developed over Missouri and Illinois during the afternoon of 13 July, and quickly moved eastward into Indiana, Michigan, and Ohio and southern Ontario province into the evening.  The initial surface soil moisture differences between the SMAP-LIS and SPoRT-LIS (Fig. 4) show that a distinct drying occurred in the data assimilation output over the Midwest, compared to the SPoRT-LIS output.  Meanwhile, a moistening occurred from SMAP DA over portions of Southern Ontario, as illustrated above.  (Note that the moist “stripe” surrounding the Great Lakes is consistent with the appearance of a moist bias near coastlines found within the SMAP Enhanced Resolution Level 2 product).  A similar signal is seen in the deeper soil layers as well.

Fig4_SM1diff

Figure 4.  Difference in initial 0-10 cm volumetric soil moisture between SMAP-LIS and SPoRT-LIS for the model runs initialized at 0000 UTC 13 July 2016.

The soil drying signal over the Midwest led to a corresponding increase in 2-m temperatures, decrease in 2-m dew points, and overall decrease in surface convective available potential energy (CAPE), as seen in the NU-WRF 18-h forecast (Fig. 5).  Meanwhile, over southern Ontario, the more moist soils in the SMAP-LIS initialized run led to an opposite response.  These changes to the simulated boundary layer environment led to an overall faster propagation of the MCS across Illinois and Indiana in the SMAP-LIS initialized NU-WRF runs, as highlighted in Fig. 6.  This faster solution was in better agreement with the observed radar reflectivity at 0000 UTC 14 July, as the NU-WRF run initialized with SPoRT-LIS data had too slow of a solution at this time.  While the two solutions converged to a slow-biased placement and timing after dark, secondary development over Southern Ontario was more aggressive in the SMAP-LIS initialized run, again in better agreement with reality (Fig. 7).  More comprehensive analysis and model verification will help us better understand the cause and effect relationship between the soil moisture initialization and the resulting NU-WRF simulation differences for this case, as well as composite results during the 2015 and 2016 warm seasons.

Fig5_T-Td-CAPEdiff

Figure 5.  Differences in NU-WRF simulated 2-m temperature (left panel; SMAP-Enhanced minus SPoRT-LIS), surface CAPE (middle), and 2-m dew point (right) for the 18-h forecast valid 1800 UTC 13 July 2016.  (Click on image for full size view)

Fig6_refl24h

Figure 6.  Comparison of NU-WRF simulated composite radar reflectivity for the (a) SPoRT-LIS initialized run, (c) SMAP-LIS initialized run, and (b) validating radar reflectivity observation, for the 24-h forecast valid 0000 UTC 14 July 2016. (click on image for full size view)

Fig7_refl28h

Figure 7.  Same as in Fig. 6, except for the 28-h forecast valid 0400 UTC 14 July 2016. (click on image for full size view)

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Plenty of Fresh Powder for Paralympic Winter Games in PyeongChang: Three Snowstorms in Eight Days

Plenty of Fresh Powder for Paralympic Winter Games in PyeongChang: Three Snowstorms in Eight Days

The 13th Paralympic Winter Games are set to begin officially in PyeongChang on March 9th, and the mountainous Olympic venues in eastern South Korea have had no shortage of snow in the last week.  Three major winter storms have affected the Korean Peninsula since 28 February 2018, helping to recharge the snowpack for the Paralympic Winter Games.  Figure 1 shows 24-hour simulated snowfall totals from SPoRT’s real-time NASA Unified-Weather Research and Forecasting (NU-WRF) model for the three recent snowstorms on 28 February, 4 March, and 7-8 March.  SPoRT is continuing to generate 24-hour forecasts of NU-WRF model runs, updated four times per day as part of the research field campaign known as the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP).

Fig1a_icepop_20180228-0000_f02400_asnowtotd03Fig1b_icepop_20180304-0600_f02400_asnowtotd03

Fig1c_icepop_20180307-1200_f02400_asnowtotd03

Figure 1.  Simulated 24-hour accumulated snowfall (in cm) from NU-WRF simulations of the snowstorms occurring over the Korean Peninsula on (a) 28 February, (b) 4 March, and (c) 7-8 March 2018.  The region depicted is the inner-nested NU-WRF model grid with 1-km horizontal spacing.

 

The Korea Meteorological Administration’s surface analysis on 0300 UTC 28 February shows a potent low pressure approaching the Korean Peninsula from the southwest (Fig. 2), which eventually intensified to less than 970 mb near northern Japan the next day.  A picture taken of the NASA Precipitation Imaging Package after the 28 February storm (Fig. 3) shows the substantial snowpack resulting from the ~40 cm (~16 inch) snowfall that occurred at the research station labeled “DGRWC” in the NU-WRF simulated snowfall plots of Figure 1.

 

Fig2_WeatherMap_2018022803

Figure 2.  Surface analysis from 0300 UTC 28 February 2018, courtesy of the Korea Meteorological Administration (KMA).

 

Fig3_SnowPicturefromWalt_20180302_202139624

Fig3bottom_PIP_snowflakes

Figure 3.  (top) Photograph taken of the NASA Precipitation Imaging Package (PIP) at the NASA instrumentation site in South Korea, following the snowstorm of 28 February. (bottom) NASA PIP and disdrometers observe a large number of 2.5+ cm diameter snowflakes/aggregates during 28 February.  Photograph at top taken by Mr. Kwonil Kim, Ph.D. student at Kyungpook National Univ.  Bottom image provided by Larry Bliven, NASA GSFC/Wallops Flight Facility.

 

Perhaps the most interesting of the three events is the latest storm from 7-8 March.  The NU-WRF model simulated composite radar reflectivity at 30-minute intervals (Fig. 4) shows a shield of moderate to heavy synoptic precipitation associated with the low pressure tracking to the south of the Olympic venues.  As the precipitation shield pulls away after ~0600 UTC 8 March, surface winds increase from a northeasterly direction over the Sea of Japan and push residual moisture inland against the mountains oriented parallel to the coastline.  This leads to a prolonged band of shallow, but moderately intense snowfall in the mountains as the moist onshore flow is forced upward by the topography.  Consequently, snowfall amounts are enhanced along the east coast of the Korean Peninsula.  Finally, the evolution from deep synoptically-driven snowfall to the shallower forced uplift snowfall is captured nicely by NU-WRF time-height cross sections at the various Olympic venues.  Figure 5 shows one of these time-height sections at the Alpensia site (location labeled in Fig. 1 panels), depicting the deep snowfall mixing ratios until ~0600 UTC 8 March, followed by a transition to much shallower, episodic snowfall for the remainder of the time period through 1800 UTC 8 March.

 

Fig4_comprefld03_2018030712_anim

Figure 4.  Twenty-hour hour animation of NU-WRF simulated composite radar reflectivity (dBZ) at 30-minute intervals from the model run initialized on 1200 UTC 7 March 2018.

 

Fig5_icepop_20180307-1800_f02400_precthgtalpd03

Figure 5.  Time-height cross-section of simulated precipitation microphysics in the lowest 2000 meters above ground level at the Alpensia Olympic venue, from the NU-WRF model run initialized on 1800 UTC 7 March 2018.

Shallow Snow and High Wind Event of 14 February during the PyeongChang2018 Winter Olympics

As the Winter Olympics come to a close this weekend, NASA/SPoRT continues its involvement in the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP) through the gathering of field campaign observations and numerical weather prediction (NWP) model data.  The ICE-POP campaign extends through March to support the Paralympic Games, and obtain more event data to set the stage for future research activities.  During the first week of the 2018 PyeongChang Winter Olympics, another weather event worth highlighting is the shallow snow and high wind episode that disrupted downhill skiing competition at Jeongseon Hill on 14 February.  On this day, a potent shortwave trough embedded in strong northwesterly flow approached the Korean Peninsula (Fig. 1), which led to a relatively short-lived, but potent snow event accompanied by strong winds in the mountains, occurring mainly between 0000-0600 UTC 14 February.

Fig1_500isotd01_2018021312_anim

Figure 1.  Animation of NASA Unified-WRF model 3-hourly 500-mb geopotential height (dam) and wind speed (m/s), valid between 1200 UTC 13 February to 1200 UTC 14 February 2018.

An animation of Himawari 10.4-micron infrared imagery from 1200 UTC 13 Feb to 1200 UTC 14 Feb (Fig. 2) shows enhanced cold cloud tops northwest of the Korean Peninsula associated with the shortwave.  However, between 0000-0600 during the snow event, we see relatively warm cloud top temperatures over the Korean Peninsula, indicative of the shallow nature of the snow.  Himawari visible imagery between ~0000-0800 UTC 14 February (Fig. 3) shows the presence of the low clouds that dissipate rapidly in coverage after 0600 UTC.  Experimental ICE-POP disdrometer measurements of hydrometeor size distribution confirm the timing of the snow event between 0000-0600 UTC (Fig. 4), showing predominantly small diameter hydrometeors (most likely snow).  However, the vertical “spikes” seen in Figure 4 between 0000-0200 UTC indicate some larger diameter snow aggregates associated with the more intense snow activity. Cloud profiling radar data (not shown) confirmed a shallow a nature to the precipitation, generally under 2 km depth.

Fig2_himawari_lwir_20180213-14

Figure 2.  Animation of Himawari 10.4 micron infrared imagery between 1200 UTC 13 February and 1200 UTC 14 February 2018.

Fig3_himawari_vis_20180214

Figure 3.  Animation of Himawari visible imagery between 0000 and 0800 UTC 14 February.

Fig4_disdrometer_timeSeries

Figure 4.  Experimental ICE-POP distrometer measurements, showing the concentration and size distribution of hydrometeors as a function of UTC hour on 14 February 2018.

The experimental NASA Unified-Weather Research and Forecasting (NU-WRF) model simulations being provided to South Korea during the Olympics captured this event fairly well.  Simulated composite radar reflectivity on the 1-km nested grid from the 1200 UTC 13 February model initialization (Fig. 5) shows a region of enhanced precipitation occurring between ~0000 to 0600 UTC 14 February, around the time of the observed snowfall.  The experimental NU-WRF run also depicts strong 10-m wind speeds during this time (orange shades exceeding 20 m/s, or ~45+ mph), particularly along the axis of higher terrain in the eastern Korean Peninsula (Fig. 6).  Finally, a time-height cross section of the NU-WRF simulated precipitation microphysics at Jeongseon Hill (Fig. 7) shows the precipitation episode timed between ~0000-0600 UTC 14 February, quite consistent with observational data.  The model also captured the shallow nature of the event, with the most substantial snow and graupel mixing ratios being primarily at or below ~1500 m above ground.

The combination of these experimental observations and NWP model data being collected during the Winter Olympics will serve as a foundation for future research to improve our understanding of snow processes in complex terrain.  Additionally, hydrometeor size distribution data from Fig. 4 along with other observations can help refine NWP model microphysical parameterization schemes to determine the proper distribution of precipitation species produced by the model.

Fig5_comprefld03_2018021312_anim

Figure 5. SPoRT/NU-WRF simulated composite radar reflectivity (dBZ) every 30 minutes on the 1-km nested grid centered on the ICE-POP Olympics venues, for the model run initialized at 1200 UTC 13 February 2018. Valid times are from 1200 UTC 13 February to 1200 UTC 14 February.

Fig6_maxwind10md03_2018021312_anim

Figure 6.  Same as in Fig. 5, except for the maximum 30-minute interval 10-meter wind speeds.

Fig7_icepop_20180213-1200_f02400_precthgtjeod03

Figure  7.  Time-height cross section of SPoRT/NU-WRF model simulated precipitation mixing ratios (g/kg) from the 1-km nested grid, valid between 1200 UTC 13 Feb and 1200 UTC 14 Feb 2018 at the Jeongseon Hill Olympics site for the lowest 2 km above ground.

High Winds Impacting Olympic Events Captured by NASA/SPoRT Model and Satellite Products

High Winds Impacting Olympic Events Captured by NASA/SPoRT Model and Satellite Products

As summarized in a previous blog post, NASA/SPoRT is providing one of many numerical weather prediction (NWP) model solutions to South Korea during the 2018 PyeongChang Winter Olympic and Paralympic Games during February and March.  The field campaign is known as the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP). In combination with the suite of radar, satellite, and in situ observations during the field campaign, the SPoRT configuration of the NASA Unified-Weather Research and Forecasting (NU-WRF) will serve as a benchmark for future research to improve our understanding of snowfall in complex terrain, our ability to estimate snow using satellites, and for improving prediction models that parameterize these intricate processes.

A key component of the Olympics field campaign is to improve forecast models through comparison to observations and satellite retrieval products.  The constellation of passive microwave imagers being assembled in support of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) precipitation dataset also provide information on near-surface meteorology necessary to estimate the surface turbulent fluxes. Algorithms designed to retrieve surface temperature, humidity, and wind speed are used together with bulk-flux algorithms to estimate the latent and sensible heat fluxes over the ocean surface. These fluxes are a source of energy and moisture for the overlying atmosphere.  One of the research goals of ICE-POP is to improve heat and moisture fluxes in prediction models through assimilation of these retrieval products.

During this past weekend, the Men’s Downhill Alpine was postponed until Thursday, and the Women’s Giant Slalom Qualifiers were canceled due to high winds that impacted Jeongseon Hill.  Figure 1 shows a 24-hour animation of NU-WRF simulated maximum 10-m wind speeds in 30-minute intervals on 11 February, on the 1-km nested grid centered on the Olympic venues.  We can see a substantial maximum in wind speed impacting the mountains along the eastern Korean Peninsula as well as offshore in the Sea of Japan.  Simulated wind speeds reached 15-20+ m s-1 (~35-45 mph) in the vicinity of Jeongseon and other mountain Olympic locations.  Wind speed observations at nearby Daegwallyeong (north-east of Jeongseon; not shown) peaked around 13 m s-1 (~30 mph) on 11 February, but speeds were most likely stronger in the higher terrain around Jeongseon.  In this particular situation, the higher resolution of the 1-km grid was critical to resolve the fine-scale variations in wind speeds within the complex terrain.

Figure1_maxwind10md03_2018021100_anim

Figure 1.  Twenty-four hour animation of NU-WRF simulated 10-m maximum wind speeds in 30-minute increments, valid from 0000 UTC 11 February through 0000 UTC 12 February 2018.

Meanwhile, the 10-m wind speeds, sensible, and latent heat fluxes are shown in Figure 2, comparing the 9-km model grid simulation with the satellite flux retrievals produced by NASA/SPoRT.  In Figure 2, the retrievals are hourly-averaged composites produced for the ICE-POP campaign, derived from swaths of the constellation of passive microwave satellites.  As the bitter cold Siberian air mass flows over the warmer open waters of the Sea of Japan, Yellow Sea, and western Pacific Ocean, substantial heat and moisture fluxes are directed from the sea surface to the atmosphere.

The 10-m model and retrieved wind speeds both depict a similar broad pattern of high wind speeds up to and exceeding 15 m s-1 across favored corridors downwind of the Korean Peninsula, China, and Russia (Figs. 2a and b).  The model sensible heat flux on the 9-km grid valid at 0600 UTC 12 February (panel c) has a broad pattern similar to the retrieval composite (panel d), but with an axis exceeding 500 W m-2 from the east coast of the Korean Peninsula to central Japan, and a broader amplitude between ~200-400 W m-2, generally higher than the retrievals values  The model latent heat flux (panel e) shows a similar pattern, except for a larger coverage of values exceeding 500 W m-2 between the Korean Peninsula and Japan, and offshore of central and southern Japan.  The maxima offshore of Japan show good agreement between the model and retrieval patterns (panels e and f).

The NU-WRF flux amplitudes are generally higher than that of the retrieval, likely due to several factors such as the retrieval being an hourly-averaged composite compared to instantaneous model fluxes, differences in product resolution, input sea surface temperatures, and model errors in simulated wind speed, and near-surface temperatures and moisture.  Following the Olympics, additional research as part of ICE-POP will involve examining the viability and benefits of assimilating the surface meteorology retrievals into the model for improving the predictions of oceanic heat and moisture transports into the atmosphere and their attendant impacts on air-mass modification.

Figure2_fluxComparison_match

Figure 2. Comparison between NU-WRF 6-h forecast and passive-microwave hourly-averaged composite retrievals of 10-m wind speed (m s-1), sensible, and latent heat flux (W m-2) valid 0600 UTC 11 February 2018. (a) NU-WRF 10-m wind speed, (b) 10-m wind speed retrieval, (c) NU-WRF sensible heat flux, (d) sensible heat flux retrieval, (e) NU-WRF latent heat flux, and (f) latent heat flux retrieval.

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.

Fig1_icepop_domain

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.

tmp2m_d03_2018020712_anim

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.