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DOE OSTI2020

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Impact of Assimilating Cloud-Cleared and Adaptively Thinned Infrared Hyperspectral Data on Tropical Cyclones in a Global Data Assimilation and Forecast Framework

A simple adaptive thinning methodology for Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder (CrIS) and Infrared Atmospheric Sounding Interferometer (IASI) radiances is evaluated through a combination of Observing System Experiments (OSEs) and adjoint methodologies. In addition, the impact of cloud-cleared radiances for AIRS is also evaluated. The OSEs are performed with the NASA Goddard Earth Observing System (GEOS, version 5) data assimilation and forecast model. The adaptive strategy uses a denser coverage in a moving domain centered around tropical cyclones (TCs), sparser everywhere else.The OSEs consist of three sets of data assimilation runs that cover the period from September 1st to 10 November 2014, with the first 20 days discarded for spin-up. All sets assimilate conventional and satellite observations used operationally. In addition, one ingests clear-sky AIRS, CrIS, and IASI radiances at different densities, another AIRS cloud-cleared radiances, and CrIS and IASI clear-sky radiances, and the third adaptively thinned AIRS, CrIS and IASI radiances. Daily 10-day forecasts are initialized from all these analyses and evaluated with focus on TCs over the Atlantic and the Pacific.Evidence is provided that this simple TC-centered adaptive radiance thinning strategy, in full agreement with previous theoretical studies, increases the global forecast skill and improves tropical cyclone representation and intensity forecast. In addition, the impact of AIRS cloud-cleared radiances is demonstrated to be particularly strong on TCs. The implications are that cloud-cleared radiances, if thinned more aggressively than the currently used clear-sky radiances, could be operationally used with large gain in TC forecasting and no loss of global skill.

Reale, Oreste↗

DOE OSTI2021

An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis

Uncertainty in predicting solar energy resources introduces major challenges in power system management and necessitates the development of reliable probabilistic solar forecasts. As the first part of the development of probabilistic forecasts based on the Weather Research and Forecasting model with solar extensions (WRF-Solar), this study presents a tangent linear approach to identify input variables responsible for the largest uncertainties in predicting surface solar irradiance and clouds. A tangent linear analysis is capable of efficiently investigating sensitivities of output variables with respect to various input variables of WRF-Solar because this approach avoids the computational burden of perturbing the initial conditions of individual input variables. We develop tangent linear models (TLMs) for six WRF-Solar physics packages that control the formation and dissipation of clouds and solar radiation, and we evaluate the validity of TLMs using a linearity test. The tangent linear sensitivity analysis is conducted under various scenarios based on satellite observations and model simulations to consider realistic input conditions. A simple method is used to quantify the impact of the uncertainty of input variables on the output variables from the TLMs. The results demonstrate that uncertainties in the output variables that are the focus of this study—including global horizontal irradiance, direct normal irradiance, cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes—are highly sensitive to uncertainties in 14 input variables. This study indicates that the tangent linear method can identify key variables of physics modules in WRF-Solar that can be stochastically perturbed to generate ensemble-based probabilistic forecasts.

14 SOLAR ENERGY↗

DOE OSTI2022

Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain

Abstract. Terrain-induced flow phenomena modulate wind turbine performance and wake behavior in ways that are not adequately accounted for in typical wind turbine wake and wind plant design models. In this work, we simulate flow over two parallel ridges with a wind turbine on one of the ridges, focusing on conditions observed during the Perdigão field campaign in 2017. Two case studies are selected to be representative of typical flow conditions at the site, including the effects of atmospheric stability: a stable case where a mountain wave occurs (as in ∼ 50 % of the nights observed) and a convective case where a recirculation zone forms in the lee of the ridge with the turbine (as occurred over 50 % of the time with upstream winds normal to the ridgeline). We use the Weather Research and Forecasting Model (WRF), dynamically downscaled from the mesoscale (6.75 km resolution) to microscale large-eddy simulation (LES) at 10 m resolution, where a generalized actuator disk (GAD) wind turbine parameterization is used to simulate turbine wakes. We compare the WRF–LES–GAD model results to data from meteorological towers, lidars, and a tethered lifting system, showing good qualitative and quantitative agreement for both case studies. Significantly, the wind turbine wake shows different amounts of vertical deflection from the terrain and persistence downstream in the two stability regimes. In the stable case, the wake follows the terrain along with the mountain wave and deflects downwards by nearly 100 m below hub height at four rotor diameters downstream. In the convective case, the wake deflects above the recirculation zone over 40 m above hub height at the same downstream distance. Overall, the WRF–LES–GAD model is able to capture the observed behavior of the wind turbine wakes, demonstrating the model's ability to represent wakes over complex terrain for two distinct and representative atmospheric stability classes, and, potentially, to improve wind turbine siting and operation in hilly landscapes.

17 WIND ENERGY↗

NASA NTRS2005

Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System (GLDAS) Land Surface States

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest agreement with the US rain gauge dataset for all seasons except winter. The CMAP precipitation was also the most closely correlated in time with the rain gauge data during spring, fall, and winter, while the satellitebased estimates performed best in summer. The GLDAS simulations revealed that modeled soil moisture is highly sensitive to precipitation, with differences in spring and summer as large as 45% depending on the choice of precipitation input.

Gottschalck, Jon↗

NASA NTRS1987

Transport models for numerical forecast

The explosive growth of computing power, coupled with scientific and technological emphasis on a national scale, has led to significant major advances in operational numerical weather prediction (NWP) during the last two decades. There are about half a dozen major centers around the world running global NWP models operationally. Many more countries have operational hemispheric or limited-area models which provide weather forecasts. The global models typically have several hundred kilometer resolution, while the limited-area models usually have horizontal spacing of 50 to 100 km. Given the pace of burgeoning growth in this area, it seems warranted to occasionally take an overview of aspects of the field common to all modelers. Here, a brief look is taken at the nature of subgrid scale turbulence transport parameterization, and some of the difficulties pertaining thereto, with particular emphasis on operational NWP models.

Burk, Stephen D.↗

NASA NTRS2015

Results of Lunar Impact Observations During Geminid Meteor Shower Events

Meteoroids are natural particles with origins from comets, asteroids, and planets from within the solar system. On average, 33 metric tons (73,000 lb) of meteoroids hit Earth everyday with velocities ranging between 20 and 72 km/s. However, the vast majority of these meteoroids disintegrate in the atmosphere and never make it to the ground. The Moon also encounters the same meteoroid flux, but has no atmosphere to stop them from striking the surface. At such speeds even a small meteoroid has incredible energy. A meteoroid with a mass of only 5 kg can excavate a crater over 9 m across, hurling 75 metric tons (165,000 lb) of lunar soil and rock on ballistic trajectories above the lunar surface. Meteoroids with particle sizes as small as 100 micrometer (1 Microgram) can do considerable damage to spacecraft in Earth's orbit and beyond. Impacts can damage thermal protection systems, radiators, windows, and pressurized containers. Secondary effects might include partial penetration or pitting, local deformation, and surface degradation that can cause a failure upon reentry. The speed, mass, density, and flux of meteoroids are important factors for design considerations and mitigation during operations. Lunar operations (unmanned and manned) are also adversely affected by the meteoroid flux. Ejecta from meteoroid impacts is also part of the lunar environment and must be characterized. Understanding meteoroid fluxes and the associated risk of meteoroids impacting spacecraft traveling in and beyond Earth's orbit is the objective of the Meteoroid Environment Office (MEO) located at Marshall Space Flight Center (MSFC). One of the MEO's programs is meteoroid impact monitoring of the Moon. The large collecting area of the night side of the lunar disk provides statistically significant counts of meteoroids that can provide useful information about the flux of meteoroids in the hundreds of grams to kilograms size range. This information is not only important for characterizing the lunar environment associated with larger lunar impactors, but also provides statistical data for verification and improving meteoroid prediction models. Current meteoroid models indicate that the Moon is struck by a sporadic meteoroid with a mass greater than 1 kg over 260 times per year. This number is very uncertain since observations for objects in this mass range are few. Factors of several times, higher or lower, are easily possible. Meteor showers are also present to varying degrees at certain times of the year. The Earth experiences meteor showers when encountering the debris left behind by comets, which is also the case with 2 the Moon. During such times, the rate of shower meteoroids can greatly exceed that of the sporadic background rate for larger meteoroids. Looking for meteor shower impacts on the Moon at about the same time as they occur on Earth will yield important data that can be fed into meteor shower forecasting models, which can then be used to predict times of greater meteoroid hazard on the Moon. The Geminids are one such meteor shower of interest. The Geminids are a major meteor shower that occur in December with a peak intensity occurring usually during the 13th and 14th of the month and appearing to come from a radiant in the constellation Gemini. The Geminids are interesting in that the parent body of the debris stream is an asteroid, which along with the Quadrantids, are the only major meteor showers not originating from a comet. The Geminids parent body, 3200 Phaethon, is about 5 km in diameter and has an orbit that has a 22deg inclination which intersects the main asteroid belt and has a perihelion less than half of Mercury's perihelion distance. Thus, its orbit crosses those of Mars, Earth, Venus, and Mercury. The Geminid debris stream is by far the most massive as compared to the others. When the Earth passes through the stream in mid-December, a peak intensity of approx. equal 120 meteors per hour can be seen. Because of the Geminids' relatively large intensity and unique origin, it is important to monitor and gain information about the Geminids so as to improve their forecasts and understand their contribution to the meteoroid environment in Earth's orbit and at the Moon. It is the purpose of this Technical Memorandum (TM) to document two lunar observing periods coinciding with the Geminid meteor showers that occurred in 2006 and 2010.

Suggs, R. J.↗

NASA NTRS1991

A simplified wind vector algorithm for satellite scatterometers

A simplified algorithm is derived for retrieving wind vectors from microwave scatterometer observations. The azimuthal dependence of the sea-surface radar cross section is modeled by a double-cosine function, rather than the traditional second-order cosine expansion. The algorithm is tested using the aircraft scatterometer observations obtained during the AAFE-RADSCAT Experiment in 1975 and 1976. There is little difference between the performance of the simplified algorithm and the more involved least-squares searching algorithm. The AAFE-RADSCAT aircraft observations are sampled so as to simulate the three-look satellite scatterometer NSCAT that will be launched on the Japanese Advanced Earth Observing Satellite. The results of the retrievals indicate that it is probably not possible to uniquely determine the wind direction using just NSCAT observations because of a 180 degree ambiguity. However, if forecast models can predict the wind direction to an accuracy of +/- 90 deg, thereby eliminating the ambiguity, then the results indicate that NSCAT can determine the wind direction to an accuracy of about +/- 20 deg. Better performance is obtained when the three observations are the same polarization (either v-pol or h-pol), as compared to using a mix of v-pol and h-pol observations.

Wentz, Frank J.↗

DOE OSTI2020

Assessing Uncertainties and Approximations in Solar Heating of the Climate System

In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate-relevant scales. We build benchmarking capability around a solar heating code (Solar-J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice-cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m -2 using a full climate re-creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ~2 W m -2 with greater latitudinal biases and are likely to affect a model’s ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.

58 GEOSCIENCES↗

NASA NTRS2000

Chandra X-Ray Observatory's Radiation Environment and the AP-8/AE-8 Model

The Chandra X-ray Observatory (CXO) was launched on July 23, 1999 and reached its final orbit on August 7, 1999. The CXO is in a highly elliptical orbit, approximately 140,000 km x 10,000 km, and has a period of roughly 63.5 hours (approx. 2.6 days). It transits the Earth's Van Allen belts once per orbit during which no science observations can be performed due to the high radiation environment. The Chandra X-ray Observatory Center (CXC) currently uses the National Space Science Data Center's "near Earth" AP-8/AE-8 radiation belt model to predict the start and end times of passage through the radiation belts. However, our scheduling software only uses a simple dipole model of the Earth's magnetic field. The resulting B, L magnet coordinates, do not always give sufficiently accurate predictions of the start and end times of transit of the Van Allen belts. We show this by comparing to the data from Chandra's on-board radiation monitor, the EPHIN (Electron, Proton, Helium Instrument particle detector) instrument. We present evidence that demonstrates this mis- of the radiation belts as well as data that also demonstrate the significant variability of one radiation belt transit to the next as experienced by the CXO. We present an explanation for why the dipole implementation of the AP-8/AE-8 gives inaccurate results. We are also investigating use of the Magnetospheric Specification and Forecast Model (MSM) - a model that also accounts for radiation belt variability and geometry.

Virani, S. N.↗

NASA NTRS1998

Incorporating Parallel Computing into the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

Atmospheric data assimilation is a method of combining actual observations with model forecasts to produce a more accurate description of the earth system than the observations or forecast alone can provide. The output of data assimilation, sometimes called the analysis, are regular, gridded datasets of observed and unobserved variables. Analysis plays a key role in numerical weather prediction and is becoming increasingly important for climate research. These applications, and the need for timely validation of scientific enhancements to the data assimilation system pose computational demands that are best met by distributed parallel software. The mission of the NASA Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. The system used to create these datasets is the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The core components of the the GEOS DAS are: the GEOS General Circulation Model (GCM), the Physical-space Statistical Analysis System (PSAS), the Observer, the on-line Quality Control (QC) system, the Coupler (which feeds analysis increments back to the GCM), and an I/O package for processing the large amounts of data the system produces (which will be described in another presentation in this session). The discussion will center on the following issues: the computational complexity for the whole GEOS DAS, assessment of the performance of the individual elements of GEOS DAS, and parallelization strategy for some of the components of the system.

Larson, Jay W.↗

DOE OSTI2026

Quantifying the Effect of Economic Development Zones on Electrical Load Growth in Kentucky [Slides]

The Kentucky Energy and Environment Cabinet has recently undertaken a comprehensive effort to map and catalog potential economic development sites across the state. The purpose of this technical assistance is to quantify the potential impact of developing designated sites on Kentucky's electricity load growth, providing insights at both state and county level considering the next 10 years. This analysis should explicitly incorporate and address key project uncertainties by developing various load growth scenarios that account for development scale, site specificity, and sector variability. The need for a site-specific analysis comes from the understanding that conventional econometric (top-down) load forecasting models cannot sufficiently isolate or predict the discrete load increases resulting from the development of these unique and targeted economic sites.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Biological particles and aerosol-cloud interactions in the Southern Great Plains (Final Report)

The United States Southern Great Plains (SGP) is the genesis for much of the warm season precipitation in the central and eastern United States. While atmospheric thermodynamics and large-scale dynamics play an important role in formation of precipitation, precipitation mechanisms are also sensitive to aerosols. Aerosols can suppress precipitation as cloud condensation nuclei (CCN) or act as ice nucleating particles (INP) in deep convective clouds. This project focused on understanding the role of primary biological aerosol particles (PBAP) in the region and its influence on cloud formation. Specifically, we focused on biological aerosol in the form of pollen, one type of PBAP that is emitted in large yet variable quantities from vegetation in the mid latitudes. Field observations provide evidence of pollen in the planetary boundary layer and pollen components in cloud droplets and fine particulate matter. Further, pollen grains can easily rupture when wet, forming smaller, sub pollen particles with sizes less than one micron. We evaluated the potential for PBAP events at the Department of Energy (DoE) SGP Atmospheric Radiation Measurement (ARM) research facility due to the rich dataset available. We also developed model simulations that accounted for pollen emission and the generation of sub pollen particles, which have been shown to be both cloud condensation nuclei and ice nucleating particles. The proposed work was designed to address the following questions: 1. What are the physical and chemical signatures of biological aerosol such as pollen in SGP ARM measurements? 2. What is the role of pollen-derived particles on deep convection and precipitation in the Central US? Using measurements from the DoE SGP ARM site and recent airborne campaigns, we evaluated the signatures of pollen and pollen-derived aerosols on optical properties, cloud properties and precipitation (Subba et al., 2021). We identified pollen-driven events over the data record and used the Weather Research and Forecasting Model with fully coupled chemistry (WRF-Chem) to conduct chemically realistic simulations of aerosols during and summer mesoscale convective events in the Southern Great Plains (Subba et al., in review). This work improved our understanding of the role of biological aerosol on clouds and precipitation in the Central United States and placed these results in context with anthropogenically-driven processes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Biological particles and aerosol-cloud interactions in the Southern Great Plains

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

The North American Monsoon GPS Transect Experiment 2013

Northwestern Mexico experiences large variations in water vapor on seasonal time scales in association with the North American monsoon, as well as during the monsoon associated with upper-tropospheric troughs, mesoscale convective systems, tropical easterly waves, and tropical cyclones. Together these events provide more than half of the annual rainfall to the region. A sufficient density of meteorological observations is required to properly observe, understand, and forecast the important processes contributing to the development of organized convection over northwestern Mexico. The stability of observations over long time periods is also of interest to monitor seasonal and longer-time-scale variability in the water cycle. For more than a decade, the U.S. Global Positioning System (GPS) has been used to obtain tropospheric precipitable water vapor (PWV) for applications in the atmospheric sciences. There is particular interest in establishing these systems where conventional operational meteorological networks are not possible due to the lack of financial or human resources to support the network. We provide an overview of the North American Monsoon GPS Transect Experiment 2013 in northwestern Mexico for the study of mesoscale processes and the impact of PWV observations on high-resolution model forecasts of organized convective events during the 2013 monsoon. Some highlights are presented, as well as a look forward at GPS networks with surface meteorology (GPS-Met) planned for the region that will be capable of capturing a wider range of water vapor variability in both space and time across Mexico and into the southwestern United States.

58 GEOSCIENCES↗

NASA NTRSDate not supplied

New York Ecological Forecasting: Utilizing NASA Earth Observations to Map Ash Distribution and Inform Emerald Ash Borer Control

Since their first sightings in the U.S. in 2002, emerald ash borer beetles (Agrilus planipennis; EAB) have killed millions of native ash (Fraxinus spp.) trees across 35 states. Infected ash stands frequently exhibit complete mortality, with the predicted result being the functional extinction of native ash in U.S. forests. In August of 2020, EAB was discovered in the 6.1-million-acre Adirondack Park. The team’s partners at the Adirondack Park Invasive Plant Program (APIPP) desired ash tree distribution and EAB susceptibility information to help improve EAB bio-control efficiency and apply the methodology to future invasive programs. To assist, the team mapped ash tree distribution using NASA Earth observations from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Shuttle Radar Topography Mission (SRTM), along with hyperspectral imagery from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data from the Monitoring and Managing Ash (MaMA) project, iMapInvasives and iNaturalist databases, and the New York State Department of Environmental Conservation (NYSDEC) provided ground truthing for mapping and modeling. Results indicate that for ash detection, the team’s Spectral Angle Mapping (SAM) hyperspectral classification is slightly more sensitive but less accurate than multispectral Random Forest (RF) classification, though neither method was above a ~20% detection rate. End products include maps of ash extent derived from both imagery types, a model forecasting future spread scenarios based on current EAB presence, and outreach materials. These products inform APIPP’s management decisions and facilitate public awareness of EAB’s threat to communities within the region.

Liam Megraw↗

NASA NTRS1991

Observing system simulation experiments for the laser atmospheric wind sounder using global spectral model

Fundamental to improving the understanding of the total Earth system are increased and improved observations. In the coming decade several spaceborne instrumented platforms will be constructed and implemented. These platforms will, in large, be housing the NASA Earth Observing System (EOS) instrument suite. One of the proposed instruments is a wind profiling system which is currently referred to as the Laser Atmospheric Wind Sounder (LAWS). This instrument will use a CO2 Doppler lidar wind profiler to give wind measurements with a vertical and horizontal resolution which has yet to be seen globally. The LAWS instrument is now a candidate for launch on a NASA EOS-B platform and is fundamental to increasing our understanding of Earth system science. The LAWS data sets will form an integral component of the temporally continuous data base needed for research of the coupled climate systems. This instrument's observations will aid in giving an improved description of the atmospheric circulation, including the transports of energy, momentum, moisture, trace gases, and aerosols. Also, the wind data will be assimilated and used as the initial state for many global forecast models at various operational centers. Results of system simulation experiments are discussed, and future experiments are described.

Rohaly, Gregg↗

NASA NTRS2016

Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release)

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.

SMAP L4_SM↗