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NASA NTRS2012

Assessment and Enhancement of MERRA Land Surface Hydrology Estimates

The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-ofthe-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979-present. This study introduces a supplemental and improved set of land surface hydrological fields ("MERRA-Land") generated by re-running a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim (ERA-I) reanalysis. MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 US basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies.

Reichle, Rolf H.↗

NASA NTRS2013

The Hyperspectral Infrared Imager (HyspIRI) Public Health and Air Quality Applications

The neglected tropical diseases (NTDs), a group of chronic, debilitating, and poverty-promoting parasitic, bacterial, and some viral and fungal infections, are among the most common causes of illness of the poorest people living in developing countries. Abiotic environmental factors are important in determining the distribution of disease-causing vectors and their life-cycles. HyspIRI observations can be merged through a Land Data Assimilation System (LDAS) be used to drive spatially-explicit ecological models of NTD vectors distribution & life cycles. Assimilations will be driven by observational data LDAS and satellite-derived meteorological forcing data, parameter datasets, and assimilation observations. HyspIRI hyperspectral measurements would provide global measurements of surface mineralogy and biotic crusts important in accessing the impact of dust in human health. HyspIRI surface thermal measurements would also help identify the variability of dust sources due to surface moisture conditions and map mineralogy.

Luvall, Jeffrey C.↗

NASA NTRS2013

The Hyperspectral Infrared Imager (HyspIRI) Public Health and Air Quality Applications

Luvall, Jeffrey C.↗

NASA NTRS2013

The Hyperspectral Infrared Imager (HyspIRI) Public Health & Air Quality Applications

Luvall, Jeffrey C.↗

NASA NTRS2014

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Department

Flooding and drought are two key forecasting challenges for the Kenya Meteorological Department (KMD). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the boundary layer of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-end events over east Africa. KMD currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Nonhydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over eastern Africa. Two organizations at the National Aeronautics and Space Administration Marshall Space Flight Center in Huntsville, AL, SERVIR and the Short-term Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMD for enhancing its regional modeling capabilities. To accomplish this goal, SPoRT and SERVIR will provide experimental land surface initialization datasets and model verification capabilities to KMD. To produce a land-surface initialization more consistent with the resolution of the KMD-WRF runs, the NASA Land Information System (LIS) will be run at a comparable resolution to provide real-time, daily soil initialization data in place of interpolated Global Forecast System soil moisture and temperature data. Additionally, real-time green vegetation fraction data from the Visible Infrared Imaging Radiometer Suite will be incorporated into the KMD-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service. Finally, model verification capabilities will be transitioned to KMD using the Model Evaluation Tools (MET) package, in order to quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. The transition of these MET tools will enable KMD to monitor model forecast accuracy in near real time. This presentation will highlight preliminary verification results of WRF runs over east Africa using the LIS land surface initialization.

Case. Jonathan↗

NASA NTRS2024

Improved Soil Moisture Estimation and Detection of Irrigation Signal By Incorporating SMAP Soil Moisture Into the Indian Land Data Assimilation System (ILDAS)

Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).

Soil Moisture↗

DOE OSTI2023

Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval

Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

Meteorology & Atmospheric Sciences↗

NASA NTRS1992

Lightning Imaging Sensor (LIS) for the Earth Observing System

Not only are scientific objectives and instrument characteristics given of a calibrated optical LIS for the EOS but also for the Tropical Rainfall Measuring Mission (TRMM) which was designed to acquire and study the distribution and variability of total lightning on a global basis. The LIS can be traced to a lightning mapper sensor planned for flight on the GOES meteorological satellites. The LIS consists of a staring imager optimized to detect and locate lightning. The LIS will detect and locate lightning with storm scale resolution (i.e., 5 to 10 km) over a large region of the Earth's surface along the orbital track of the satellite, mark the time of occurrence of the lightning, and measure the radiant energy. The LIS will have a nearly uniform 90 pct. detection efficiency within the area viewed by the sensor, and will detect intracloud and cloud-to-ground discharges during day and night conditions. Also, the LIS will monitor individual storms and storm systems long enough to obtain a measure of the lightning flashing rate when they are within the field of view of the LIS. The LIS attributes include low cost, low weight and power, low data rate, and important science. The LIS will study the hydrological cycle, general circulation and sea surface temperature variations, along with examinations of the electrical coupling of thunderstorms with the ionosphere and magnetosphere, and observations and modeling of the global electric circuit.

Christian, Hugh J.↗

NASA NTRS2023

Investigating the Response of Land-Atmosphere Interactions and Feedbacks to Spatial Representation of Irrigation in a Coupled Modeling Framework

The transport of water, heat, and momentum from the surface to the atmosphere is dependent in part on the 10 characteristics of the land surface. Together with the model physics, parameterization schemes, and parameters employed, land datasets determine the spatial variability in land surface states (i.e., soil moisture and temperature) and fluxes. Despite the importance of these datasets, they are often chosen out of convenience or regional limitations without due assessment of their impacts on model results. Irrigation is an anthropogenic form of land heterogeneity that has been shown to alter the land surface energy balance, ambient weather, and local circulations. As such, irrigation schemes are becoming more 15 prevalent in weather and climate models with rapid developments in dataset availability and parameterization scheme complexity. Thus, to address pragmatic issues related to modeling irrigation, this study uses a high-resolution, regional coupled modeling system to investigate the impacts of irrigation dataset selection on land-atmosphere (L-A) coupling using a case study from the Great Plains Irrigation Experiment (GRAINEX) field campaign. The simulations are assessed in the context of irrigated versus non-irrigated regions, subregions across the irrigation gradient, and sub-grid scale process 20 representation in coarser scale models. The results show that L-A coupling is sensitive to the choice of irrigation dataset and resolution and that the irrigation impact on surface fluxes and near surface meteorology can be dominant, conditioned on the details of the irrigation map (i.e., boundaries, heterogeneity, etc), or minimal. A consistent finding across several analyses was that even a low percentage of irrigation fraction (i.e., 4-16%) can have significant local and downstream atmospheric impacts (e.g., lower PBL height), suggesting that representation of boundaries and heterogeneous areas within irrigated 25 regions is particularly important for the modeling of irrigation impacts on the atmosphere in this model. When viewing the simulations presented here as a proxy for ‘ideal’ tiling in a Earth System Model scale gridbox, the results show that some ‘tiles’ will reach critical nonlinear moisture and planetary boundary layer (PBL) thresholds that could be important for clouds and convection, implying that heterogeneity resulting from irrigation should be taken into consideration in new sub-grid land-atmosphere exchange parameterizations.

Patricia Lawston-Parker↗

NASA NTRS1993

Cloud fields derived from satellite and surface data during FIRE cirrus phase 2

The interpretation of surface and aircraft measurements of cloud properties taken during field programs must take into account the large-scale cloud and meteorological conditions. Cloud properties are also required at scales beyond the point and line data taken from ground and aircraft platforms. Satellite data can provide a quantitative description of these large-scale cloud properties. When derived from geostationary satellite data, the cloud fields constitute a unique source for evaluating the development and demise of a cloud system. Satellites, however, can only see the tops of clouds, so that cloud layers below the uppermost cloud deck may remain undetected resulting in a incomplete depiction of the cloud system. Some multilayer clouds are amenable to detection from satellites. Many, especially in midlatitude cyclonic systems, can only be observed from the surface. A combination of surface and satellite cloud observations should be the most complete quantification of large-scale cloudiness if there are sufficient surface measurements. During the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment Phase 2 (FIRE-2) Cirrus Intensive Field Observation (IFO) period (November 13 - December 7, 1991) conducted at Coffeyville, Kansas, cirrus observations were taken in a variety of conditions. The IFO area was selected for a variety of reasons including the relatively dense network of surface weather stations and special surface instrumentation sites. Thus, the FIRE-2 IFO presents an excellent opportunity to combine cloud observations from surface and satellite observations. This paper presents an analysis of cloud properties on a mesoscale grid using satellite cloud property retrievals, surface observer data, and rawinsonde temperature and humidity profiles.

Minnis, Patrick↗

DOE OSTI2026

Improving High Resolution Offshore Wind Resource Assessments and Forecasts using Observations in the MA/RI Lease Areas

The third Wind Forecast Improvement Project (WFIP3) sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the Marine Atmospheric Boundary Layer (MABL). WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high use coastal zone, using a 3-D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the MABL over the ocean was done from an air-sea interaction flux tower and extended deployments of a large barge platform. WFIP3 focused on mesoscale and sub-mesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. Numerous critical forecasting phenomena were observed, however the project was terminated prior to the completion of the field observational period and the analysis period.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

The Hyperspectral Infrared Imager (HyspIRI) Public Health and Air Quality Applications

The neglected tropical diseases (NTDs), a group of chronic, debilitating, and poverty-promoting parasitic, bacterial, and some viral and fungal infections, are among the most common causes of illness of the poorest people living in developing countries. Abiotic environmental factors are important in determining the distribution of disease-causing vectors and their life-cycles. HyspIRI observations can be merged through a Land Data Assimilation System (LDAS) be used to drive spatially-explicit ecological models of NTD vectors distribution and life cycles. Assimilations will be driven by observational data LDAS and satellite-derived meteorological forcing data, parameter datasets, and assimilation observations. HyspIRI hyperspectral measurements would provide global measurements of surface mineralogy and biotic crusts important in accessing the impact of dust in human health. HyspIRI surface thermal measurements would also help identify the variability of dust sources due to surface moisture conditions and map mineralogy.

Luvall, Jeffrey C.↗

NASA NTRSDate not supplied

Status and Operations at the Granite Island, Michigan (GIM) BSRN Station

In June 2018, a new surface radiation site was established on Granite Island, Michigan (GIM), located in Lake Superior. GIM is a 0.1 square km (2.5 acre) rock island. GIM moved from candidate to active and became a fully-fledged member of the Baseline Surface Radiation Network (BSRN) in July 2020 (BSRN Label: GIM). The installation is solar powered, and autonomous instrument functions are accessed remotely from Hampton, Virginia, USA. The original motivation to establishing surface radiation measurements at GIM was to combine high quality downwelling shortwave and longwave measurements with existing evaporation measurements that could lead to improved understanding of the Earth’s energy budget. Other scientific benefits are the addition of a new "water" site to the BSRN network (Water sites are rare in the BSRN network) and a surface validation site for satellite measurements such as the Clouds and the Earth’s Radiant Energy System (CERES). In this poster, we present the status of GIM. GIM is equipped with standard downwelling flux at visible and infrared wavelengths as required by the BSRN network. Basic meteorological parameters are being monitored, as well as other data collections for aerosol and total column water vapor, PAR, and lake skin temperature. Lastly, the capabilities to clean shortwave optics remotely will be discussed and a current list of instruments and pictures will be displayed.

Bryan Fabbri↗

NASA NTRSDate not supplied

Improving the Representation of Land Surface Processes Using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here, we emphasize the implementation of tools and approaches to overcome challenges related to land DA to constrain carbon and water cycling. In particular, we discuss the implementation of adaptive inflation to modify ensemble spread in response to time-varying networks of gridded observations. We also discuss methods to generate ensemble spread through boundary condition (meteorology) forcing that can be applied to site-level applications. Next, we describe the application of vertical localization upon surface soil moisture observations, and forward operators specifically designed for the assimilation of snow and solar-induced fluorescence observations. Finally, we discuss the potential benefit of a quantile conserving filter used to update bounded quantities (state or parameter values).

Brett Raczka↗

NASA NTRS1986

Data communications and monitor for the Penn State University profiler network

The profiler network installed by the Department of Meteorology at Penn State University utilizes a microcomputer for network monitoring and control. The network consists of two VHF and one UHF wind profiling Doppler radars. Additional measurement systems added to the network include temperature and humidity profiling radiometers, sodar for boundary layer wind profiling and selected surface based baseline systems. Remote diagnostic capabilities were also implemented in the Penn State network. It is possible to remotely analyze many specific malfunctions of the transmitter or signal processor.

Peters, R. M.↗

NASA NTRS1992

Lithological and textural controls on radar and diurnal thermal signatures of weathered volcanic deposits, Lunar Crater region, Nevada

Radar backscatter intensity as measured by calibrated synthetic aperture radar (SAR) systems is primarily controlled by three factors: local incidence angle, wavelength-scale roughness, and dielectric permittivity of surface materials. Radar observations may be of limited use for geological investigations of surface composition, unless the relationships between lithology and the above characteristics can be adequately understood. In arid terrains, such as the Southwest U.S., weathering signatures (e.g. soil development, fracturing, debris grain size and shape, and hill slope characteristics) are controlled to some extent by lithologic characteristics of the parent bedrock. These textural features of outcrops and their associated debris will affect radar backscatter to varying degrees, and the multiple-wavelength capability of the JPL Airborne SAR (AIRSAR) system allows sampling of textures at three distinct scales. Diurnal temperature excursions of geologic surfaces are controlled primarily by the thermal inertia of surface materials, which is a measure of the resistance of a material to a change in temperature. Other influences include albedo, surface slopes affecting insolation, local meteorological conditions and surface emissivity at the relevant thermal wavelengths. To first order, thermal inertia variations on arid terrain surfaces result from grain size distribution and porosity differences, at scales ranging from micrometers to tens of meters. Diurnal thermal emission observations, such as those made by the JPL Thermal Infrared Multispectral Scanner (TIMS) airborne instrument, are thus influenced by geometric surface characteristics at scales comparable to those controlling radar backscatter. A preliminary report on a project involving a combination of field, laboratory and remote sensing observations of weathered felsic-to basaltic volcanic rock units exposed in the southern part of the Lunar Crater Volcanic Field, in the Pancake Range of central Nevada is presented. Focus is on the relationship of radar backscatter cross sections at multiple wavelengths, apparent diurnal temperature excursions identified in multi-temporal TIMS images, surface geometries related to weathering style, and parent bedrock lithology.

Plaut, Jeffrey J.↗

NASA NTRS2014

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

Flooding and drought are two key forecasting challenges for the Kenya Meteorological Service (KMS). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the planetary boundary layer (PBL) of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface, particularly within weakly-sheared environments such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in land surface and numerical weather prediction (NWP) models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-impact weather over eastern Africa. KMS currently runs a configuration of the Weather Research and Forecasting (WRF) NWP model in real time to support its daily forecasting operations, making use of the NOAA/National Weather Service (NWS) Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the KMS-WRF runs on a regional grid over eastern Africa. Two organizations at the NASA Marshall Space Flight Center in Huntsville, AL, SERVIR and the Shortterm Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMS for enhancing its regional modeling capabilities through new datasets and tools. To accomplish this goal, SPoRT and SERVIR is providing enhanced, experimental land surface initialization datasets and model verification capabilities to KMS as part of this collaboration. To produce a land-surface initialization more consistent with the resolution of the KMS-WRF runs, the NASA Land Information System (LIS) is run at a comparable resolution to provide real-time, daily soil initialization data in place of data interpolated from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model soil moisture and temperature fields. Additionally, realtime green vegetation fraction (GVF) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi- NPP) satellite will be incorporated into the KMS-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service (NESDIS). Finally, model verification capabilities will be transitioned to KMS using the Model Evaluation Tools (MET; Brown et al. 2009) package in conjunction with a dynamic scripting package developed by SPoRT (Zavodsky et al. 2014), to help quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. Furthermore, the transition of these MET tools will enable KMS to monitor model forecast accuracy in near real time. This paper presents preliminary efforts to improve land surface model initialization over eastern Africa in support of operations at KMS. The remainder of this extended abstract is organized as follows: The collaborating organizations involved in the project are described in Section 2; background information on LIS and the configuration for eastern Africa is presented in Section 3; the WRF configuration used in this modeling experiment is described in Section 4; sample experimental WRF output with and without LIS initialization data are given in Section 5; a summary is given in Section 6 followed by acknowledgements and references.

Case, Johnathan L.↗

NASA NTRS2022

2022 Power Global Community (Gloco) Summit Summary Report

The Prediction Of Worldwide Energy Resources (POWER) Project 25th year of providing global solar and meteorology data products to the world. The goals of this event were to inform users of new, enhanced features, gather feedback on services, identify new requests and requirements, and capture more thorough user stories while identifying new user communities and partnership opportunities. The event was “virtual” and had over 580 registrants. Over the two-day event, the Summit was attended by 162 unique attendees representing 24 countries.

NASA↗