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.


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.


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


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.



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 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.


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.


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)


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)


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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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.


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.


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.

Soil Moisture Conditions over Southeast Texas Prior to Hurricane Harvey

Soil Moisture Conditions over Southeast Texas Prior to Hurricane Harvey

As much-anticipated Hurricane Harvey approaches the southern and eastern coast of Texas today, it is worth examining the pre-existing soil moisture over the region to understand the capacity of the land surface to absorb the upcoming rainfall.  Granted, the amount of rainfall simulated by numerical guidance is off-the-charts high (e.g., today’s 0600 UTC initialized NAM model [Fig. 1] shows 84-hour maximum accumulated rainfall of over 60″ between Corpus Christie and Houston!!).  Thus, extreme flooding is anticipated, regardless of the amount that can be absorbed by the soils.


Figure 1.  The NCEP/NAM model 84-hour forecast of total accumulated precipitation (inches) over Southeastern Texas, from the simulation initialized at 0600 UTC 25 August 2017 [image courtesy of College of DuPage forecast page].

SPoRT manages a real-time simulation of the NASA Land Information System (hereafter, “SPoRT-LIS“), running over the Continental U.S. at ~3-km grid 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).  The climatological simulation spans 1981-2013 and forms the basis for daily-updated total-column soil moisture percentiles (forthcoming in Fig. 3), in order to place current soil moisture values into historical context.  For real-time output, the Noah simulation is regularly updated four times per day as an extension of the long-term climatology simulation.  It includes 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 climatological SPoRT-LIS is based exclusively on atmospheric analysis input from the NOAA/NASA North American Land Data Assimilation System – version 2.

Relative Soil Moisture output from the SPoRT-LIS over the 0-100 cm layer is shown in Fig. 2 over Southeastern Texas and Louisiana at 1200 UTC this morning.  A marked gradient between very dry soils to the west and moist soils to the east occurs in the vicinity of the greater Houston metropolitan area.  The soils in the region bounded by Corpus Christi, San Antonio, Austin, and Houston (areas forecast to have the greatest rainfall from Hurricane Harvey) are extremely dry prior to Harvey’s landfall.  This dryness will help to some extent in absorbing the initial rainfall from Hurricane Harvey.  But with such excessive rainfall being forecast over a prolonged time period (3-5+ days), it won’t be long before the upper portions of the soil column saturates and widespread areal flooding occurs.  In addition, the high forecast rainfall rates could easily result in flash flooding (despite prevailing soil dryness), especially further inland where terrain plays a more important role in runoff and flash flooding.

The total column relative soil moisture percentile from 24 August shows that historically-speaking, the soil moisture is slightly drier than normal, particularly along the coastal plain between Corpus Christi and Houston (Fig. 3).  In this corridor, the soil moisture is generally between the 10th and 30th percentile compared to the 1981-2013 climatological distribution for 24 August.


Figure 2.  SPoRT-LIS relative soil moisture (RSM) distribution in the 0-1 meter layer across Southeastern Texas and Louisiana, valid 1200 UTC 25 August 2017.  RSM values of 0% represent wilting (vegetation cannot extract moisture from soil) and 100% represents saturation (subsequent rainfall becomes runoff).


Figure 3.  Total column (0-2 m) relative soil moisture percentile valid 24 Aug 2017, as compared to all 24 August soil moisture values from a 33-year climatological simulation of the SPoRT-LIS.

Finally, an hourly animation of the 1-day changes in 0-10 cm (top model layer) relative soil moisture show that the near-surface soils are quickly moistening between Corpus Christi and Houston, as the initial rainbands of Hurricane Harvey began impacting the coastal plain this morning.  As the soils continue to moisten rapidly from the top-down, subsequent rainfall will quickly lead to runoff and flooding.


Figure 4.  Hourly animation of 1-day change in top-layer (0-10 cm) relative soil moisture, for the time period spanning 0000-1400 UTC 25 August 2017.  Each hourly image is a simple difference in 0-10 cm relative soil moisture between the current and previous day at the same valid hour.  Line contours depict one-hour QPE from the MRMS product, as input to the real-time SPoRT-LIS.

Multiple Atmospheric Rivers Impact California in Early 2017

The state of California has been suffering from a multi-year drought that has severely depleted water resources and reservoir levels. Recent winters have failed to produce precipitation and mountain snows to replenish the losses during the dry summers. However, the situation has rapidly changed this winter, particularly in the past week when multiple atmospheric rivers have impacted the state.

An atmospheric river is a concentrated channel of deep moisture that is transported from the tropical Pacific Oceanic regions to the West Coast of the United States.  These events are often associated with prodigious amounts of rainfall and mountain snows that lead to flooding, mudslides, and avalanches.  We have seen such events this past week impact California, especially the central and northern parts of the state.  CIRA’s total precipitable water product in Figures 1a and 1b depict two separate atmospheric rivers impinging on central California from 8 and 10 January 2017, respectively. The first wave transported a plume of tropical moisture from the south-southwest, which led to massive rainfall and high snow levels.  The second atmospheric river on the 10th was less directly connected to the tropics (coming in from the west-southwest), but nonetheless exhibited a well-focused transport of high moisture content.  Widespread flooding and mountain avalanches have resulted from these moisture plumes as the impacted California, as well as dramatic replenishment of reservoirs.


Figure 1.  CIRA total precipitable water product (inches) valid at (a) 2100 UTC 8 Jan 2017, and (b) 2100 UTC 10 Jan 2017.


SPoRT’s real-time instantiation of the Land Information System (aka “SPoRT-LIS”) has nicely depicted the substantial replenishment of the moisture content in the soils over California.  The SPoRT-LIS is an observations-driven, ~3-km resolution run of the Noah land surface model that consists of a 33-year climatology spanning 1981-2013, and real-time output at hourly intervals sent to select NOAA/NWS partnering forecast offices.  The one-year change in the SPoRT-LIS total column soil moisture at 1200 UTC 11 January (Fig. 2) shows large increases over most of California, particularly in the higher terrain (given by blue and purple shading).  [At the same time, annual degradation in soil moisture can be seen across the central and eastern U.S.]  Interestingly, a substantial portion of California’s annual soil moisture increases has occurred in just the past week (Fig. 3; SPoRT-LIS total column soil moisture change over the past week).  One can certainly see the important role that atmospheric rivers play in being “drought busters”!


Figure 2.  One-year change in the SPoRT-LIS total column relative soil moisture, valid 1200 UTC 11 January 2017.



Figure 3.  One-week change in the SPoRT-LIS total column relative soil moisture, valid 1200 UTC 11 January 2017.


A map of the SPoRT-LIS daily soil moisture percentiles from 11 January highlight the very wet anomaly over California relative to the 33-year soil moisture climatology (Fig. 4; similar to the pattern of annual soil moisture change from Fig. 2).  Blue shading denotes greater than or equal to the 98th percentile, thus indicating unusually wet soils on the tail end of the historical distribution.


Figure 4.  SPoRT-LIS total column relative soil moisture percentile from 11 January 2017.


Finally, SPoRT is acquiring and assimilating in real time the Soil Moisture Active Passive (SMAP) Level 2 (L2) retrievals produced by NASA/JPL into an experimental version of the SPoRT-LIS.  SPoRT is a SMAP Early Adopter and has a funded project to conduct soil moisture data assimilation experiments with LIS and evaluate impacts on land surface and numerical weather prediction models.  Figure 5 shows SMAP L2 retrievals of the evening overpasses from ~0000 UTC 11 January.  Panel (a) is the 36-km resolution radiometer product, while panel (b) shows the enhanced-resolution product, obtained from the SMAP radiometer by using Backus-Gilbert optimal interpolation techniques to provide data on a finer (9 km) grid.  The enhanced-resolution product provides much more detail of the wet soils in California, while retaining the same overall regional patterns as the original 36-km retrieval.  Given the loss of the active radar component of the SMAP mission, SPoRT plans to assimilate both the 36-km and 9-km products separately, and compare results on model accuracy.


Figure 5. SMAP Level 2 soil moisture retrievals for the evening overpasses from ~0000 UTC 11 January 2017; (a) 36-km resolution product; (b) enhanced 9-km resolution product.

U.S. Deep South flooding as depicted by NASA’s SMAP satellite and SPoRT-LIS

U.S. Deep South flooding as depicted by NASA’s SMAP satellite and SPoRT-LIS

A significant flooding event occurred over the U.S. Deep South from 8-10 March 2016 due to a slow-moving low pressure/front from southern Texas to the Mississippi River, combined with a deep tropical moisture plume.  Tremendous rainfall totals of 4-8″+ were depicted by the Multi-Radar Multi-Sensor (MRMS) 1-km gauge-corrected radar rainfall estimate product for consecutive 24-hour periods ending 1200 UTC 9 March and 10 March 2016 (Fig. 1).  The MRMS gridded product provides short-term input precipitation estimates to the real-time Land Information System (LIS) run at the NASA Short-term Prediction Research and Transition (SPoRT) Center.


Fig. 1.  Rainfall estimates from the Multi-Radar Multi-Sensor (MRMS) gauge-adjusted radar product for the 24-h period ending (a) 1200 UTC 9 Mar 2016, and (b) 1200 UTC 10 Mar 2016.

The soil moisture response to the heavy rainfall was captured nicely by NASA’s Soil Moisture Active-Passive (SMAP) satellite Level 2 retrieval product of 0-5 cm volumetric soil moisture for the early morning overpass across the Central U.S from 9 March (Fig. 2a).  Very high retrieved volumetric soil moisture of 0.45 or higher is seen from eastern Texas across much of northern Louisiana and southern/central Arkansas, aligned well with the areas that received the heaviest rainfall.  The corresponding SPoRT-LIS modeled soil moisture analysis for the 0-10 cm layer of the Noah land surface model (Fig. 2b) shows a reasonable agreement with the SMAP retrieved soil moisture but with slightly less dynamic range than the SMAP data — a reasonable result given that the SMAP retrieval is valid over a shallower, near-surface layer than the LIS top model layer.


Fig. 2.  (a) Soil Moisture Active-Passive (SMAP) 0-5 cm volumetric soil moisture retrieval valid 1223 UTC 9 Mar 2016, and (b) corresponding SPoRT-Land Information System (LIS) 0-10 cm volumetric soil moisture analysis valid 1200 UTC 9 Mar 2016.

The SPoRT-LIS total column relative soil moisture (RSM) has been found to qualitatively correlate with areas of river/areal flooding when exceeding ~60% across northern Alabama.  The depiction of total column RSM from the early morning of 10 March shows a large area exceeding 65% (blue  shading in Fig. 3a) across eastern Texas, northern Louisiana, and southern/central Arkansas.  The weekly change in total column RSM (Fig. 3b) highlights the regions that experienced the largest increases in soil moisture in response to the heavy rainfall.  As the soil approaches saturation/field capacity, most new rainfall goes directly to runoff, thereby exacerbating the flooding situation.


Fig. 3.  SPoRT-LIS analysis valid 1200 UTC 10 Mar 2016 of (a) total column relative soil moisture (RSM), and (b) one-week change in total column RSM.

Another product available from the real-time SPoRT-LIS is the total column RSM percentile, which shows how anomalous the current soil moisture conditions are relative to a historical, 33-year climatological database of modeled soil moisture. The percentile product from the morning of 10 March (Fig. 4) shows that areas of eastern Texas and northwestern Louisiana (and a few other spots) exceed the 98th percentile for the current day — indicating that these present-day soil moisture values are about the most moist it has been in the last 33+ years for 10 March.

Notice that there are a few anomalous “bulls-eyes” of dry percentiles in southern/eastern Arkansas.  These areas are caused by corrupt input precipitation data driving the SPoRT-LIS land surface model simulation.  The issue is currently being corrected by the operational organizations managing the rain gauge input.  However, it should be noted that SPoRT is working to implement near real-time data assimilation of the SMAP Level 2 soil moisture retrievals into its LIS simulation.  Routine assimilation of SMAP satellite soil moisture will help correct anomalies caused by poor input precipitation, thereby resulting in more robust soil moisture analyses for situational awareness and disaster-response applications.


Fig. 4. SPoRT-LIS total column RSM percentile valid 1200 UTC 10 Mar 2016.

Finally, the areas of active flooding at U.S. Geological stream gauges and the NOAA/NWS flood watch/warning map are given in Fig. 5 for the afternoon of 10 March.  The axis of flash flood warnings (dark red color) aligns quite well with the total column RSM percentiles exceeding the 98th percentile in Fig. 4, whereas the broader footprint of all flood warnings/watches corresponds closely with the total column RSM above the 65% value (blue shading in Fig. 3a).


Fig. 5.  Plot of U.S. Geological Survey stream gauges with active flooding (top), and NOAA/NWS active [flash] flood watches and warnings (bottom-right) for the afternoon of 10 Mar 2016.

SFR Product Verifies Snow Coverage over Four Corners

The NESDIS Snowfall Rate (SFR) product assessment is in full swing at NWS Albuquerque and forecasters are already capturing some good cases over data sparse regions. The first week of January 2016 was very active across New Mexico as back to back winter storm systems crossed the area. The second system in the series crossed over the Four Corners region on 4 January 2016, producing light to moderate snowfall rates for several hours. The forecaster on shift noted the observation at Farmington, NM (KFMN) indicated light snow with a visibility of 5 statute miles. A quick glance at the SFR procedure used in Figure 1a shows the extent of any precipitation echoes well to the east of KFMN at 0000 UTC 5 January 2016. The nearest radar (KABX, not shown) is located roughly 150 miles southeast of KFMN near Albuquerque, NM. The arrival of a SFR product at 0010 UTC 5 January 2016 showed the extent of the precipitation was much greater with the merged POES image overlaid on the radar data (Figure 1b). Sampled liquid equivalent values in the light green areas to the east of KFMN were near 0.03″/hour.

Figure 1a. Liquid equivalent values of the merged SFR product valid 0000 UTC 5 January 2016. KFMN is denoted by the white circle. Note the extent of the radar coverage is well east of KFMN.

Figure 1b. Liquid equivalent values of the merged SFR product valid 0010 UTC 5 January 2016. KFMN is denoted by the white circle. Note the extent of the snowfall coverage is much greater with the addition of the POES image.

The Terminal Aerodrome Forecast (TAF) issued for KFMN shortly before the receipt of this image indicated temporary fluctuations in the visibility to 1 statute mile with light snow and an overcast ceiling near 1,200 ft between 0000 UTC and 0400 UTC (Instrument Flight Rules, IFR). It is not clear whether any operational changes occurred based on the receipt of the merged SFR product or whether the product increased confidence on the IFR forecast. However, it is entirely possible given the improvement in product latency compared to the 2015 assessment that the imagery could be used in this way.

The webcam available at San Juan College just a short distance from the KFMN observation showed significant decreases in the visibility between 330pm and shortly after sunset (Figure 2a and 2b). The two images below show the decrease in surface visibility as well as notable accumulations on grassy surfaces in front of the college. An observer 3 miles southeast of Farmington did report a total accumulation of 1″ from this event. The merged SFR product did in fact show higher rates immediately to the east of KFMN. The last image in the series shows the impact on travel conditions noted by the NM Department of Transportation web page (Figure 3). The areal coverage of the difficult travel impacts (yellow highlights) was greater than that depicted by what can be seen based on poor radar coverage.

Figure 2a. Webcam at San Juan College around 330pm. Note the light snowfall beginning to develop over the distant mesas behind the college.

Figure 2a. Webcam at San Juan College around 330pm. Note the light snowfall beginning to develop over the distant mesas behind the college.

Figure 2b. Webcam at San Juan College shortly after sunset. Note the dramatic decrease in visibility and light snow accumulations on grassy surfaces in front of the college.

Figure 2b. Webcam at San Juan College shortly after sunset. Note the dramatic decrease in visibility and light snow accumulations on grassy surfaces in front of the college.

Figure 3. Screen capture of NM DOT web page showing areal coverage of difficult travel conditions (yellow highlights) and some text summaries detailing the impacts.

Heavy morning snow on 03Mar2014 with some freezing rain far southeast



During the weekend of Mar 02-03 2014, several weather features moved northeast across the area. The precipitation started out as rain across West Virginia with some freezing rain, sleet and snow across portions of southeast Ohio. Colder air began to filter into the region and as it did, the precipitation changed from rain to freezing rain to sleet and finally to snow. By 603 AM, the precipitation had turned to snow across all of West Virginia, but for portions of the extreme southeast counties.

I have attached two images from around 6 AM on Mar 3rd. The first image showed the radar data from KRLX at 603 am while the second image was the 607 am SFR product and 6 AM surface observations. When comparing these images, the “best” SFR signal for heavy snow was located along a line where the precipitation transitioned from freezing rain to snow. The heaviest signal in the SFR images was actually located over Mingo County where a total of 8 inches of snow was reported from the storm.

Freezing rain was falling across portions of extreme southeast West Virginia. Bluefield WV (KBLF) is located southeast of the “SFR” heaviest snow signal in an area where the SFR product is not showing anything. The SFR product did a great job across that area as the 6 AM KBLF observation indicated freezing rain was falling at that time.