Data Assimilation Enhancements to Air Force Weathers Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoDs Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoDs only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information

Improving a Priori Regional Climate Model Estimates of Greenland Ice Sheet Surface Mass Loss Through Assimilation of Measured Ice Surface Temperatures

The Greenland ice sheet has been the focus of climate studies due to its considerable impact on sea level rise. Accurate estimates of surface mass balance components - including precipitation, runoff, and evaporation - over the Greenland ice sheet would contribute to understanding the cause of the ice sheet’s recent changes (i.e., increase in melt amount and duration, thickening of ice sheet interior, thinning at the ice sheet margins) and help to forecast future changes. Deterministic approaches provide a general trend of the surface mass fluxes, but they cannot characterize the uncertainty of estimates. The data assimilation method developed in this dissertation aimed to optimally merge the satellite-derived ice surface temperature into a snow/ice model while taking into account the uncertainty of input variables. Satellite-derived ice surface temperatures were used to improve the estimates of the Greenland ice sheet surface mass fluxes. Three studies were conducted on the Greenland ice sheet. The goal of the first study was to provide a proof of concept of the proposed methodology. A set of observing system simulation experiments was performed to retrieve the true surface mass fluxes of the Greenland ice sheet. The data assimilation framework was able to reduce the RMSE of the prior estimates of runoff, sublimation/evaporation, surface condensation, and surface mass loss fluxes by 61%, 64%, 76%, and 62%, respectively, over the nominal prior estimates from the regional climate model. In the second study, satellite-derived ice surface temperatures were assimilated into a snow/ice model. The results show that the data assimilation framework was capable of retrieving ice surface temperatures with a mean spatial RMSE of 0.3 K which was 69% less than that of the prior estimate without conditioning on satellite-derived ice surface measurements. Evaluation of surface mass fluxes is a critical part of the study; however, it is limited by the spare amount of independent data sets. Several data sets were used to investigate the feasibility of verification of results. It was found that predicted melt duration is in agreement with melt duration from passive microwave measurements; however, more efforts are needed to further verify the results. In the third study, the feasibility of microwave radiance assimilation was investigated by characterizing the error and uncertainty in predicted passive microwave brightness temperature from the radiative transfer model. We found significant uncertainty between the predicted measurement and satellite-derived passive microwave brightness temperature due to error in snow states, coarse resolution of the passive microwave and also an imperfect coupled snow/ice and radiative transfer model. Based on our findings, radiance assimilation requires more accurate snow grain size parameterization to take into account temporal and spatial variability of snow grain size. Furthermore, coarse resolution of both passive microwave brightness temperature and snow/ice model and attribute uncertainties of both predicted and measured brightness temperature make the radiance assimilation unattractive. This research demonstrates that ice surface temperature measurements have valuable information that can be extracted by a data assimilation technique to improve the estimates of the Greenland ice sheet surface mass fluxes

Irrigation characterization improved by the direct use of SMAP soil moisture anomalies within a data assimilation system

Prior soil moisture data assimilation (DA) efforts to incorporate human management features such as agricultural irrigation has only shown limited success. This is partly due to the fact that observational rescaling approaches for bias correction used in soil moisture DA systems are less effective when unmodeled processes such as irrigation are the dominant source of systematic biases. In this article, we demonstrate an alternative approach, i.e. anomaly correction for overcoming this limitation. Unlike the rescaling approaches, the proposed method does not scale remote sensing soil moisture retrievals to the model climatology, but it extracts the temporal variability information from the retrievals. The study demonstrates this approach through the assimilation of soil moisture retrievals from the Soil Moisture Active Passive mission into the Noah land surface model. The results demonstrate that DA using the anomaly correction method can better capture the effect of irrigation on soil moisture in agricultural areas while providing comparable performance to the DA integrations using rescaling approaches in non-irrigated areas. These findings emphasize the need to reduce inconsistencies between remote sensing and the models so that assimilation methods can employ information from remote sensing more directly to develop representations of unmodeled processes such as irrigation

Reanalysis Surface Mass Balance of the Greenland Ice Sheet Along K-Transect (2000–2014)

Accurate estimates of surface mass balance over the Greenland ice sheet (GrIS) would contribute to understanding the cause of recent changes and would help to better estimate the future contribution of the GrIS to sea-level rise. Given the limitations of in-situ measurements, modeling, and remote sensing, it is critical to explore the opportunity to merge the available data to better characterize the spatial and temporal variation of the GrIS surface mass balance (SMB). This work utilizes a particle batch smoother data assimilation technique that yields SMB estimates that benefit from the snow model Crocus and a 16-day albedo product derived from satellite remote sensing data. Comparison of the results against in-situ SMB measurements shows that the assimilation of the albedo product reduces the root mean square error of the posterior estimates of SMB by 51% and reduces bias by 95%

Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements