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.
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.
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)