An official website of the United States government
The Great Plains (GP) low-level jet (GPLLJ) contributes to GP warm season water resources (precipitation), wind resources, and severe weather outbreaks. Past research has shown that synoptic and local mesoscale physical mechanisms (Holton and Blackadar mechanisms) are required to explain GPLLJ variability. Although soil moisture–PBL interactions are central to local mechanistic theories, the diurnal effect of regional soil moisture anomalies on GPLLJ speed, northward penetration, and propensity for severe weather is not well known. In this study, two 31-member WRF-ARW stochastic kinetic energy backscatter scheme ensembles simulate a typical warm season GPLLJ case under CONUS-wide wet and dry soil moisture scenarios. In the GP (24°–48°N, 103°–90°W), ensemble mean differences in sensible heating and PBL height of 25–150 W m −2 and 100–700 m, respectively, at 2100 UTC (afternoon) culminate in GPLLJ 850-hPa wind speed differences of 1–4 m s −1 12 hours later (0900 UTC; early morning). Greater heat accumulation in the daytime PBL over dry soil impacts the east–west geopotential height gradient in the GP (synoptic conditions and Holton mechanism) resulting in a deeper thermal low in the northern GP, causing increases in the geostrophic wind. Enhanced daytime turbulent mixing over dry soil impacts the PBL structure (Blackadar mechanism), leading to increased ageostrophic wind. Overnight geostrophic and ageostrophic winds constructively interact, leading to a faster nocturnal GPLLJ over dry soil. Ensemble differences in CIN (~50–150 J kg −1 ) and CAPE (~500–1000 J kg −1 ) have implications for severe weather predictability.
more »
« less
Assimilation of Satellite-Derived Soil Moisture for Improved Forecasts of the Great Plains Low-Level Jet
Abstract In the context of forecasting societally impactful Great Plains low-level jets (GPLLJs), the potential added value of satellite soil moisture (SM) data assimilation (DA) is high. GPLLJs are both sensitive to regional soil moisture gradients and frequent drivers of severe weather, including mesoscale convective systems. An untested hypothesis is that SM DA is more effective in forecasts of weakly synoptically forced, or uncoupled GPLLJs, than in forecasts of cyclone-induced coupled GPLLJs. Using the NASA Unified Weather Research and Forecasting (NU-WRF) Model, 75 GPLLJs are simulated at 9-km resolution both with and without NASA Soil Moisture Active Passive SM DA. Differences in modeled SM, surface sensible (SH) and latent heat (LH) fluxes, 2-m temperature (T2), 2-m humidity (Q2), PBL height (PBLH), and 850-hPa wind speed (W850) are quantified for individual jets and jet-type event subsets over the south-central Great Plains, as well as separately for each GPLLJ sector (entrance, core, and exit). At the GPLLJ core, DA-related changes of up to 5.4 kg m −2 in SM can result in T2, Q2, LH, SH, PBLH, and W850 differences of 0.68°C, 0.71 g kg −2 , 59.9 W m −2 , 52.4 W m −2 , 240 m, and 4 m s −1 , respectively. W850 differences focus along the jet axis and tend to increase from south to north. Jet-type differences are most evident at the GPLLJ exit where DA increases and decreases W850 in uncoupled and coupled GPLLJs, respectively. Data assimilation marginally reduces negative wind speed bias for all jets, but the correction is greater for uncoupled GPLLJs, as hypothesized.
more »
« less
- Award ID(s):
- 1638936
- PAR ID:
- 10216768
- Date Published:
- Journal Name:
- Monthly Weather Review
- Volume:
- 148
- Issue:
- 11
- ISSN:
- 0027-0644
- Page Range / eLocation ID:
- 4607 to 4627
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Abstract Soil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed datasets, namely, Soil Moisture Operational Product System (SMOPS) and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola–Chattahoochee–Flint basin in the southeastern United States. The model-based estimates are then compared against the corresponding U.S. Drought Monitor (USDM) archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents; however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition.more » « less
-
Soil moisture data assimilation (SM-DA) is a valuable approach for enhancing streamflow prediction in rainfall-runoff models. However, most studies have focused on incorporating remotely sensed SM, and their results strongly depend on the quality of satellite products. Compared with remote sensing products, in situ observed SM data provide greater accuracy and more effectively capture temporal fluctuations in soil moisture levels. Therefore, the effectiveness of SM-DA in improving streamflow prediction remains site-specific and requires further validation. Here, we employed the Ensemble Kalman filter (EnKF) to integrate daily SM into lumped and distributed approaches of the Xinanjiang (XAJ) hydrological model to assess the importance of SM-DA in streamflow prediction. We observed a general improvement in streamflow prediction after conducting SM-DA. Specifically, the Nash-Sutcliffe efficiency increased from 0.61 to 0.65 for the lumped and from 0.62 to 0.70 for the distributed approaches. Moreover, the efficiency of SM-DA exhibits seasonal variation, with in situ SM proving particularly valuable for streamflow prediction during the wet-cold season compared to the dry-warm season. Notably, daily SM data from deep layers exhibit a stronger capability to improve streamflow prediction compared to surface SM. This indicates the significance of deep SM information for streamflow prediction in mountain areas. Overall, this study effectively demonstrates the efficacy of assimilating SM data to improve hydrological models in streamflow prediction. These findings contribute to our understanding of the connection between SM, streamflow, and hydrological connectivity in headwater catchments.more » « less
-
Radiosonde observations over Antarctica and the surrounding oceans were enhanced during the Year of Polar Prediction in the Southern Hemisphere (YOPP‐SH) summer Special Observing Period (SOP). Observing System Experiments (OSEs) were conducted in a continuous cycling framework using the Weather Research and Forecasting (WRF) Model and its data assimilation system. Routinely available observations were assimilated in the CTL (control) experiment, and special radiosonde observations from the YOPP‐SH SOP were additionally assimilated in the YOPP experiment. The results were compared to investigate the effects of additional radiosonde observations on analyses and forecasts over and around Antarctica. Verifications against ERA5 re‐analysis, radiosonde observations, and Automatic Weather Station (AWS) observations show overall positive effects of additional radiosonde observations. These positive effects are most noticeable in temperature at lower levels at earlier forecast lead times; afterward, wind forecast improvements at upper levels are the most noticeable. Although routine and special radiosonde observations are concentrated over the eastern and coastal regions of Antarctica (compared to the western and inland regions), the effects of the extra data spread in longitudinal and latitudinal directions; therefore, the effects on the forecasts are not limited to only the areas near the radiosonde observations. A case study reveals how cyclone forecasts are improved through the assimilation of the additional YOPP‐SH SOP radiosonde observations. This study provides insights into future observation strategies in Antarctica, such as horizontal/vertical observation locations, observation variables, and so forth to maximize effects of new observations on forecasts over Antarctica.more » « less
-
Sea surface salinity (SSS) anomalies and near-surface thermohaline stratification are key parameters to improve our understanding of sea ice retreat and formation in polar regions. Since 2010, the remote sensing salinity missions ESA Soil Moisture Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) offer unprecedented SSS observations globally (SSSSMOS and SSSSMAP, respectively). In this study, we compare these observations with in situ salinity observations (SSSin‐situ) made during the NASA salinity field campaign Salinity and Stratification at Sea Ice Edge (SASSIE) during the fall of 2022. The SASSIE SSSin‐situ were collected by nine different platforms: Castaway and Underway conductivity–temperature–depth (CTD), Wave Gliders, Thermosalinograph, Snake salinity, Surface Wave Instrument Float with Tracking (SWIFT) drifters, Upper Temperature of the Polar Oceans (UpTempO) buoys, Jet Surface Salinity Profiler (Jet-SSP), and Autonomous Lagrangian Thermometric Observer (ALTO) and Air-Launched Autonomous Micro Observer (ALAMO) profilers. Because satellite SSS retrievals are impacted by land and sea ice contaminations, cold temperatures, and surface roughness, mean differences, root-mean-square difference (RMSD), and standard deviation (STD) between satellite SSS and SSSin‐situ are examined as a function of distance from the coast and sea ice edge, sea surface temperature (SST), and wind speed. We find that SSSSMOS and SSSSMAP are well correlated (0.66 and 0.78, respectively) with similar RMSD when compared with SSSin‐situ. Close to the coast (0–150 km), SSSSMAP compares better with SSSin‐situ with RMSD (<2 g kg−1) lower than that from SSSSMOS. Near the sea ice edge (0–150 km), SSSSMOS compares better with SSSin‐situ with RMSD (<2.5 g kg−1) lower than that from SSSSMAP. In cold water (SST < 1.5°C) and low wind speed conditions (<7 m s−1), both SSSSMOS and SSSSMAP are consistent with each other. The RMSD between SSSSMAP and SSSin‐situ decreases considerably (<1 g kg−1) when SST > 1.5°C, while the RMSD between SSSSMOS and SSSin‐situ shows less dependence on SST.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1175/MWR-D-20-0185.1
