Author: Travis Swaim

245 – Develop Satellite Snow Data Assimilation Capabilities into the NASA Land Information System (LIS) to Support NWS Hydrologic Applications
Principal Investigator(s): Y. Liu

Accurate snow prediction during snow accumulation and melting periods is essential for various hydrologic and water resources applications in snow-impacted regions. However, estimates of snow water equivalent (SWE) from current land surface models typically contain significant errors due to poor model physics in representing the snow processes and other sources of uncertainties such as inaccurate model forcing. Satellite-derived snow products, albeit subject to errors themselves, hold great potential for improving model snow predictions if properly assimilated into the models. This project represents a collaborative effort between NASA GSFC and the National Operational Hydrologic Remote Sensing Center (NOHRSC) of the National Weather Service, which aims to develop snow data assimilation (DA) capabilities into the NASA Land Information System (LIS) to integrate satellite-based SWE and snow cover fraction (SCF) products into several land surface models to improve snow prediction over the Alaska domain. The developed snow DA capability will be transferred to NOHRSC to support their hydrologic forecasting in Alaska and other research domains such as Afghanistan.

237 – Assimilation of AMSR-E Soil Moisture Retrievals and Analysis of Soil Moisture Spatial Variability
Principal Investigator(s): B. Li

Terrestrial water storage (TWS), which includes soil moisture, groundwater, surface water and snow, is an important hydrological indicator and can be used to infer the potential for future hydrological stress (drought). The state of TWS is also directly linked to other aspects of the hydrological cycle such as infiltration rates and subsurface flow. Large uncertainty exists in model estimated TWS due to uncertainties in forcing, model parameters and inadequacies in model physics. The TWS variations as observed by the Gravity Recovery and Climate Experiment (GRACE) satellites, which is the only satellite mission that monitors the water storage changes in the Earth’s vertical profile, can be used to nudge model estimates towards the truth so that more objective estimates of TWS can be achieved.

233 – Evaluation of the NASA GMAO Modern Era Retrospective-Analysis for Research and Applications (MERRA) in Polar Latitudes
Principal Investigator(s): R. I. Cullather

The work performed under this task relates to high latitude process modeling and analysis. MERRA is a state-of-the-art global numerical reanalyses that has recently been produced by the NASA GMAO, and covers the period from 1979 until the present. The work associated with MERRA seeks to characterize its performance over polar regions including the Arctic Ocean and continental ice sheets and, where appropriate, apply MERRA for the purpose of regional climate study. Additionally, work is to be performed with experts in ice sheet modeling along with the GEOS-5 model development team to integrate and evaluate a community ice sheet model within the GEOS-5 modeling framework. Analysis of GEOS-5 climate simulations and GEOS-5 products is also conducted to evaluate the representation of the water and energy cycle in the model and assimilation systems at high latitudes, and to contribute to better understanding the Earth system, models and reanalyses. To that end, both in situ and remote sensing observations will be considered along with MERRA and other existing reanalyses and the climate simulations. Topics of particular interest are ice sheet surface mass balance, precipitation processes and high latitude climate processes in general. The position is part of a team, and as such, the incumbent’s expertise may be useful to other activities, generally related to atmospheric reanalysis.

229 – Evaluation of mesoscale model (WRF) and LIS-WRF under for various thunderstorm cases across continents
Principal Investigator(s): A. Kumar

Goals:
(a) To evaluate NASA developed Land Information System (LIS) in WRF (LIS coupled WRF) and also evaluate LIS spin-ups soil condition. First we have to run LIS spin-up for 10 years and evaluate simulated soil moisture, soil temperature at different soil depth, latent heat flux and sensible heat flux for over Southern Great Plains region, and compare with station flux site data for verification. In second step we ran NU-WRF coupled system and performed evaluation, our study objective is to understand the impact of different microphysics schemes on land-surface process (surface and soil parameters).

224 – Improving NOAA/NWS River Forecast Center Decision Support with NASA Satellite and Land Information System Products
Principal Investigator(s): S. Yatheendradas

The overarching goal was to demonstrate improved accuracy in runoff, stream flow, and flood monitoring and simulation that result from the combination of NASA infrastructure of snow data (MODIS) and model (LIS), with operational NOAA National Weather Service (NWS) River Forecast System (NWSRFS) Decision Support Tools (DST). The objectives were to engineer and integrate NASA satellite- and model-derived land surface products, through the Land Information System (LIS), into NWSRFS DST component models. The specific science question we investigated is whether adjusting modeled snow area with MODIS estimates improves modeled streamflow.

206 and 254 – Vegetation 3D structural parameters from multi-sensor data: Biomass mapping and change analyses from LVIS data
Principal Investigator(s): G. Sun

Lidar provides sample data for forest parameters mapping using other imagery data in a region. Most of previous studies on biomass estimation from lidar data produced satisfactory results at forest stand or plot levels, not in the lidar footprint level. To map biomass using remote sensing imagery data in large scale, the estimation of biomass at lidar waveform footprint needs to be investigated.

138 – Comparison of Air Quality Models with Satellite Observations for Improved Model Predictive Capabilities
Principal Investigator(s): C. Flynn

Satellite observations provide several important benefits to air quality, including improved forecasting ability for air quality models, assessment of air quality for attribution to specific sources, and improved estimation of source emissions. However, many challenging problems remain for the use of satellite observations in diagnosing near-surface air pollution. The column-integrated quantities retrieved from satellite instruments for key trace gases and aerosols must be interpreted correctly to derive information about near-surface conditions. Despite these challenges, a major scientific goal remains the use of satellite observations to improve and validate current air quality models for more accurate predictive capability. The DISCOVER-AQ field project provides surface, in-situ aircraft, and remote sensing data that will aid in the interpretation of satellite data for air quality. This project was conducted in support of this overall goal, by comparing satellite observations, aircraft measurements, and surface air quality datasets with air quality model output. Such a comparison may lead to better understanding of the factors affecting the correlation of satellite observations with current models.

127 – Validation and Calibration of Airborne Lidar Data Collected Using NASA’s Laser Vegetation Imaging Sensor in Support of the DESDynI Lidar Mission
Principal Investigator(s): M. Hofton

This project coordinates high-altitude airborne lidar data experiments in the Antarctic and Greenland to collect elevation and surface structure information in support of NASA’s Operation Ice Bridge. After processing and quality checking, data precision and accuracy are assessed using crossover analyses and comparison with available in situ data. Data are released publically. The study results in the collection, analysis and validation of the precision and accuracy of elevation and topographic structure products derived from the 25m-footprint, waveform LVIS lidar over ice targets.

115 – The Global Reservoir and Lake Monitoring System: Enhancing the USDA/FAS DSS with NASA, NRL and ESA Satellite Radar Altimeter Data
Principal Investigator(s): C. Birkett (ESSIC/UMD)

This program aims to enhance and expand a satellite-based, near-real time, reservoir and lake water-level monitoring system. This system is on-line, operational, existing within the US Department of Agriculture (USDA) decision support system (DSS) through the cooperative USDA/NASA Global Agricultural Monitoring (GLAM) program. Current lake level products stem from the NASA/CNES TOPEX/Poseidon (archival 1992-2002), Jason-1 (post 2002 and near real time), Jason-2/OSTM (post 2008) missions, the US Naval Research Lab’s GFO (post 2000) mission, and the current ESA ENVISAT (post 2002) mission. The primary user is the Office of Global Analysis (OGA) within the USDA Foreign Agricultural Service (FAS). The FAS utilize the products for irrigation potential considerations and as general indicators of drought and high-water conditions. The monitoring system thus has relevance to water resources management and agriculture efficiency applications at both the national and international level.

109 – NASA Decision Support: Monitoring Air Quality Effects of Anthropogenic Emissions Reductions and Estimating Emissions from Natural Sources
Principal Investigator(s): D. Allen

An accurate specification of anthropogenic and natural emissions is crucial for determining the impact of emission perturbations on air quality. However, when this project began, lightning-NO emissions, a substantial contributor to tropospheric NO2 columns over the United States during the summertime, were not included in the Community Multiscale Air Quality (CMAQ) model used by the Environmental Protection Agency. The first goal of this project was to add lightning-NO emissions to CMAQ. Simulations with lightning-NO emissions provide more accurate estimates of nitrogen deposition and are useful for top down estimates of anthropogenic emissions. The second goal of this project was to use tropospheric nitrogen dioxide (NO2) columns retrieved from the Ozone Monitoring Instrument (OMI) aboard NASA’s Aura satellite to refine emissions of nitric oxide (NO) by microbial activity in soils calculated by the Biogenic Emission Inventory System (BEIS) that is used within the EPA’s Community Multiscale Air Quality Model (CMAQ).

Lightning-NO emissions in the CMAQ model were parameterized using a previously developed method that utilizes the relationship between flash rate and convective precipitation rate. The resulting flash rate distributions were scaled so that monthly average model flash rates match observed monthly average flash rates where the “observed” flash rates were determined using National Lightning Detection Network (NLDN) cloud-to-ground (CG) flash rates for the months of interest and climatological IC (intracloud)/CG ratios. CMAQ simulations were run with the improved source distributions for lightning-NO. Results were evaluated and the resulting algorithm is included as an option in the most recent release of CMAQ.

In order to evaluate how well CMAQ captures changes in NO2 columns associated with soil-NO emissions from BEIS, we compared changes in modeled and OMI-retrieved columns following precipitation events. Our goal was to determine if changes in NO2 columns associated with soil-NO emissions were visible in the OMI data set and if the observed changes were consistent with what was modeled via simulations with CMAQ. In order to put bounds on the emissions, we also performed CMAQ simulations with no soil-NO emissions and with doubled soil-NO emissions.