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DOE OSTI2021

Hybrid (PDE+ML) models in the context of land ice modeling

Focal Area(s): Primary focal areas are: predictive modeling through the use of AI-derived model components; advanced methods including network design/optimization/deep learning. Science Challenge: The atmospheric, ocean and ice dynamics components of the Energy Exascale Earth System Model (E3SM) are governed by Partial Differential Equations (PDEs) and significant efforts have been made during the last decades to develop such computational models. Here we propose to fundamentally improve these PDE-based codes by enhancing them with Machine Learning (ML) sub-models for complex, poorly understood physical processes in the context of ice sheet modeling. We propose to train these models with a novel approach that allows the assimilation of the different sources of data available (direct/indirect observations and possibly simulation data), improving on existing simplified models. We also highlight computational challenges originating from the coexistence of PDE-based and ML-based models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 sq.km. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June–September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.

Katie Fankhauser↗

NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗

NASA NTRS2002

Calibration Plans for the Global Precipitation Measurement (GPM)

The Global Precipitation Measurement (GPM) is an international effort led by the National Aeronautics and Space Administration (NASA) of the U.S.A. and the National Space Development Agency of Japan (NASDA) for the purpose of improving research into the global water and energy cycle. GPM will improve climate, weather, and hydrological forecasts through more frequent and more accurate measurement of precipitation world-wide. Comprised of U.S. domestic and international partners, GPM will incorporate and assimilate data streams from many spacecraft with varied orbital characteristics and instrument capabilities. Two of the satellites will be provided directly by GPM, the core satellite and a constellation member. The core satellite, at the heart of GPM, is scheduled for launch in November 2007. The core will carry a conical scanning microwave radiometer, the GPM Microwave Imager (GMI), and a two-frequency cross-track-scanning radar, the Dual-frequency Precipitation Radar (DPR). The passive microwave channels and the two radar frequencies of the core are carefully chosen for investigating the varying character of precipitation over ocean and land, and from the tropics to the high-latitudes. The DPR will enable microphysical characterization and three-dimensional profiling of precipitation. The GPM-provided constellation spacecraft will carry a GMI radiometer identical to that on the core spacecraft. This paper presents calibration plans for the GPM, including on-board instrument calibration, external calibration methods, and the role of ground validation. Particular emphasis is on plans for inter-satellite calibration of the GPM constellation. With its Unique instrument capabilities, the core spacecraft will serve as a calibration transfer standard to the GPM constellation. In particular the Dual-frequency Precipitation Radar aboard the core will check the accuracy of retrievals from the GMI radiometer and will enable improvement of the radiometer retrievals. Observational intersections of the core with the constellation spacecraft are essential in applying this technique to the member satellites. Information from core spacecraft retrievals during intersection events will be transferred to the constellation radiometer instruments in the form of improved calibration and, with experience, improved radiometric algorithms. In preparation for the transfer standard technique, comparisons using the Tropical Rainfall Measuring Mission (TRMM) with sun-synchronous radiometers have been conducted. Ongoing research involves study of critical variables in the inter-comparison, such as correlation with spatial-temporal separation of intersection events, frequency of intersection events, variable azimuth look angles, and variable resolution cells for the various sensors.

Bidwell, S. W.↗

NASA NTRS2010

Satellite Gravimetry Applied to Drought Monitoring

Near-surface wetness conditions change rapidly with the weather, which limits their usefulness as drought indicators. Deeper stores of water, including root-zone soil wetness and groundwater, portend longer-term weather trends and climate variations, thus they are well suited for quantifying droughts. However, the existing in situ networks for monitoring these variables suffer from significant discontinuities (short records and spatial undersampling), as well as the inherent human and mechanical errors associated with the soil moisture and groundwater observation. Remote sensing is a promising alternative, but standard remote sensors, which measure various wavelengths of light emitted or reflected from Earth's surface and atmosphere, can only directly detect wetness conditions within the first few centimeters of the land s surface. Such sensors include the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) C-band passive microwave measurement system on the National Aeronautic and Space Administration's (NASA) Aqua satellite, and the combined active and passive L-band microwave system currently under development for NASA's planned Soil Moisture Active Passive (SMAP) satellite mission. These instruments are sensitive to water as deep as the top 2 cm and 5 cm of the soil column, respectively, with the specific depth depending on vegetation cover. Thermal infrared (TIR) imaging has been used to infer water stored in the full root zone, with limitations: auxiliary information including soil grain size is required, the TIR temperature versus soil water content curve becomes flat as wetness increases, and dense vegetation and cloud cover impede measurement. Numerical models of land surface hydrology are another potential solution, but the quality of output from such models is limited by errors in the input data and tradeoffs between model realism and computational efficiency. This chapter is divided into eight sections, the next of which describes the theory behind satellite gravimetry. Following that is a summary of the GRACE mission and how hydrological information is gleaned from its gravity products. The fourth section provides examples of hydrological science enabled by GRACE. The fifth and sixth sections list the challenging aspects of GRACE derived hydrology data and how they are being overcome, including the use of data assimilation. The seventh section describes recent progress in applying GRACE for drought monitoring, including the development of new soil moisture and drought indicator products, and that is followed by a discussion of future prospects in satellite gravimetry based drought monitoring.

Rodell, Matthew↗

NASA NTRS2015

Next-Generation Angular Distribution Models for Top-of-Atmosphere Radiative Flux Calculation from the CERES Instruments: Methodology

The top-of-atmosphere (TOA) radiative fluxes are critical components to advancing our understanding of the Earth's radiative energy balance, radiative effects of clouds and aerosols, and climate feedback. The Clouds and the Earth's Radiant Energy System (CERES) instruments provide broadband shortwave and longwave radiance measurements. These radiances are converted to fluxes by using scene-type-dependent angular distribution models (ADMs). This paper describes the next-generation ADMs that are developed for Terra and Aqua using all available CERES rotating azimuth plane radiance measurements. Coincident cloud and aerosol retrievals, and radiance measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological parameters from Goddard Earth Observing System (GEOS) data assimilation version 5.4.1 are used to define scene type. CERES radiance measurements are stratified by scene type and by other parameters that are important for determining the anisotropy of the given scene type. Anisotropic factors are then defined either for discrete intervals of relevant parameters or as a continuous functions of combined parameters, depending on the scene type. Significant differences between the ADMs described in this paper and the existing ADMs are over clear-sky scene types and polar scene types. Over clear ocean, we developed a set of shortwave (SW) ADMs that explicitly account for aerosols. Over clear land, the SW ADMs are developed for every 1 latitude1 longitude region for every calendar month using a kernel-based bidirectional reflectance model. Over clear Antarctic scenes, SW ADMs are developed by accounting the effects of sastrugi on anisotropy. Over sea ice, a sea-ice brightness index is used to classify the scene type. Under cloudy conditions over all surface types, the longwave (LW) and window (WN) ADMs are developed by combining surface and cloud-top temperature, surface and cloud emissivity, cloud fraction, and precipitable water. Compared to the existing ADMs, the new ADMs change the monthly mean instantaneous fluxes by up to 5Wm(exp -2) on a regional scale of 1 deg. latitude x 1 deg. longitude, but the flux changes are less than 0.5Wm(exp -2) on a global scale.

Su, W.↗

NASA NTRS2010

Projected Applications of a "Climate in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to "Climate in a Box" systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the "Climate in a Box" system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the "Climate in a Box" system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

Jedlovec, Gary J.↗

NASA NTRS2011

Evaluating the Application of Multi-Satellite Observations in Hydrologic Modeling

When monitoring local or regional hydrosphere dynamics for applications such as agricultural productivity or drought and flooding events, it is necessary to have accurate, high-resolution estimates of terrestrial water and energy storages. Though in-situ observations provide reliable estimates of hydrologic states and fluxes, they are only capable of accurately capturing the dynamics at relatively discrete points in space and time, which makes them inadequate for characterizing the variability of the water budget across scales. In contrast, satellite-based remote sensing is ideal for providing observations of hydrological states and fluxes because it provides spatially-distributed observations at spatial and temporal scales required for regional land surface process modeling. Due to the continued progress in algorithm development and emerging satellite technology, we now have near-real time monitoring of several components of the water cycle including precipitation, soil moisture, lake and river height, terrestrial water storage, snow cover, and evapotranspiration. As these data become more readily available, their application to hydrologic modeling is becoming more common, however there remains little consensus on the most appropriate method for optimal integration and evaluation in regard to hydrological applications. Here we present two case studies operationally applying several remotely sensed products from AMSR-E, GRACE, and MODIS and discuss assimilation strategies, ease of integration and interpretation, and methods for quantifying the success of the application methodology.

Bolten, John↗

NASA NTRS1995

Technical Report Series on Global Modeling and Data Assimilation. Volume 7: Proceedings of the Workshop on the GEOS-1 Five-year Assimilation

The primary objective of the three-day workshop on results from the Data Assimilation Office (DAO) five-year assimilation was to provide timely feedback from the data users concerning the strengths and weaknesses of version 1 of the Goddard Earth Observing System (GEOS-1) assimilated products. A second objective was to assess user satisfaction with the current methods of data access and retrieval. There were a total of 49 presentations, with about half (23) of the presentations from scientists from outside of Goddard. The first two days were devoted to applications of data: studies of the energy diagnostics, precipitation and diabatic heating, hydrological modeling and moisture transport, cloud forcing and validation, various aspects of intraseasonal, seasonal, and interannual variability, ocean wind stress applications, and validation of surface fluxes. The last day included talks from the National Meteorological Center (NMC), the National Center for Atmospheric Research (NCAR), the Center for Ocean-Land-Atmosphere Studies (COLA), the United States Navy, and the European Center for Medium Range Weather Forecasts (ECMWF).

Suarez, Max J.↗

NASA NTRS2008

Interannual Variability and Trends of Extratropical Ozone: Northern Hemisphere - Part 1

The authors apply principal component analysis (PCA) to the extratropical total column ozone from the combined merged ozone data product and the European Centre for Medium-Range Weather Forecasts assimilated ozone from January 1979 to August 2002. The interannual variability (IAV) of extratropical O-3 in the Northern Hemisphere (NH) is characterized by four main modes. Attributable to dominant dynamical effects, these four modes account for nearly 60% of the total ozone variance in the NH. The patterns of variability are distinctly different from those derived for total O-3 in the tropics. To relate the derived patterns of O-3 to atmospheric dynamics, similar decompositions are performed for the 30 100-Wa geopotential thickness. The results reveal intimate connections between the IAV of total ozone and the atmospheric circulation. The first two leading modes are nearly zonally symmetric and represent the connections to the annular modes and the quasi-biennial oscillation. The other two modes exhibit in-quadrature, wavenumber-1 structures that, when combined, describe the displacement of the polar vortices in response to planetary waves. In the NH, the extrema of these combined modes have preferred locations that suggest fixed topographical and land-sea thermal forcing of the involved planetary waves. Similar spatial patterns and trends in extratropical column ozone are simulated by the Goddard Earth Observation System chemistryclimate model (GEOS-CCM). The decreasing O-3 trend is captured in the first mode. The largest trend occurs at the North Pole, with values similar to-1 Dobson Unit (DU) yr(-1). There is almost no trend in tropical O-3. The trends derived from PCA are confirmed using a completely independent method, empirical mode decomposition, for zonally averaged O-3 data. The O-3 trend is also captured by mode 1 in the GEOS-CCM, but the decrease is substantially larger than that in the real atmosphere.

Yung, Yuk L.↗

NASA NTRS2011

Accessing Recent Trend of Land Surface Temperature from Satellite Observations

Land surface temperature (Ts) is an important element to measure the state of terrestrial ecosystems and to study surface energy budgets. In support of the land cover/land use change-related international program MAIRS (Monsoon Asia Integrated Regional Study), we have collected global monthly Ts measured by MODIS since the beginning of the missions. The MODIS Ts time series have approximately 11 years of data from Terra since 2000 and approximately 9 years of data from Aqua since 2002, which makes possible to study the recent climate, such as trend. In this study, monthly climatology from two platforms are calculated and compared with that from AIRS. The spatial patterns of Ts trends are accessed, focusing on the Eurasia region. Furthermore, MODIS Ts trends are compared with those from AIRS and NASA's atmospheric assimilation model, MERRA (Modern Era Retrospective-analysis for Research and Applications). The preliminary results indicate that the recent 8-year Ts trend shows an oscillation-type spatial variation over Eurasia. The pattern is consistent for data from MODIS, AIRS, and MERRA, with the positive center over Eastern Europe, and the negative center over Central Siberia. The calculated climatology and anomaly of MODIS Ts will be integrated into the online visualization system, Giovanni, at NASA GES DISC for easy use by scientists and general public.

Shen, Suhung↗

NASA NTRSDate not supplied

Characterization of OCO-2 and ACOS-GOSAT biases and errors for flux estimates

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~1.1 ppm, while having 2-3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5-10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

Susan S Kulawik↗

NASA NTRS1997

The Complex Stratigraphy of the Highland Crust in the Serenitatis Region of the Moon Inferred from Mineral Fragment Chemistry

Large impact basins are natural drill holes into the Moon, and their ejecta carries unique information about the rock types and stratigraphy of the lunar crust. We have conducted an electron microprobe study of mineral fragments in the poikilitic melt breccias collected from the Taurus Mountains at the Apollo 17 landing site. These breccias are virtually unanimously agreed to be impact melt produced in the Serenitatis impact event. They contain lithic fragments and much more abundant mineral fragments of crustal origin. We have made precise microprobe analyses of minor element abundances in fragments of olivine, pyroxene, and plagioclase to provide new information on the possible source rocks and the crustal stratigraphy in the Serenitatis region. These data were also intended to elucidate the nature of the cryptic geochemical component in breccias such as these with low-K Fra Mauro basalt compositions. We chose the finest-grained (i.e., most rapidly quenched) breccias for study, to avoid reacted and partly assimilated fragments as much as possible. Most of the mineral fragments appear to have been derived from rocks that would fall into the pristine igneous Mg-suite as represented by lithic fragments in the Apollo collection, or reasonable extensions of it. Gabbroic rocks were more abundant in the target stratigraphy than is apparent from the Apollo sample collection. Some pyroxene and plagiociase, but probably not much olivine, could be derived from feldspathic granulites, which are metamorphosed polymict breccias. Some mineral fragments are from previously unknown rocks. These include highly magnesian olivines (up to Fo(sub 94)), possibly volcanic in origin, that exacerbate the difficulty in explaining highly magnesian rocks in the lunar crust. It appears that some part of the lunar interior has an mg*(= 100 x Mg/(Mg/Fe) atomic) greater than the conventional bulk Moon value of 80-84. Other volcanic rocks, including mare basalts, and rapidly- cooled impact melt rocks do not contribute significantly to the fragment population. Nor do ferroan anorthosites contribute more than a tiny part of even the plagiociase fragment population. A few mineral fragments that are consistent with the cryptic low-K Fra Mauro chemical component were found, and these appear to be from gabbroic sources. The mineral fragment populations cannot be mixed in their observed proportions to produce the whole rock composition, because the fragments are more refractory and deficient in Ti, P, and alkalis. A preferential contribution to the melt from a rock similar to sodic ferrogabbro can partly resolve the discrepancy. The population of mineral fragments requires a very diverse population of igenous rocks that are not all related to each other, demonstrating the existence of a complex crust built of numerous separate igneous plutons. Many of these plutons may have crystallized at shallow depths. The chemical composition of the melt breccias, in combination with the mineral fragment data and an understanding of the cratering process, suggests that the deepest crust sampled by the Serenitatis impace (not necessarily the deepest crust) was basaltic in composition, including KREEP and gabbroic rocks like sodic ferrogabbro, and lacking abundant olivine-rich material. These were overlain by Mg-suite rocks of varied types, including norites and troctolites that supplied most of the olivine mineral fragments. Granulities, which are metamorphosed and more feldspathic breccias, were abundant near the surface. Remote sensing indicates that the entire Serenitatis region lacks ferroan anorthosite, consistent with the results of our study.

Ryder, Graham↗

DOE OSTI2023

Matrix Approach to Accelerate Spin-Up of CLM5

Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin-up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi-Analytical Spin-Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin-up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin-up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin-up on site and globally in comparison with other spin-up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2003

Time-Variable Gravity Signal due to Extratropic Pacific Water Mass Redistribution

Using the satellite-laser-ranging (SLR) data, Cox and Chao [2002] reported the detection of a large post-1998 anomaly (in the form of a positive jump) in the time series of Earth s lowest-degree gravity harmonic 52, or the dynamic oblateness. Among several groups now examining the mass redistribution in the global geophysical fluids in search of the cause(s), we report here a temporally coinciding anomalies found in the extratropic north + south Pacific basins. Clearly seen in the leading EOFPC mode for extratropic Pacific, these anomalies occurred in sea-surface height, sea-surface temperature, and temperature- and salinity-depth profiles. We based our analysis on two different data sources: TOPEX/Poseidon altimetry, and the ECCO ocean general circulation model output assimilating T/P data. The magnitude of these changes, when converted to equivalent J2 change, appears to be a few times too small to explain the observed J2 directly. These findings, and the fact that the anomalies occurred following the strong 1997-98 El Nino, suggest strong geophysical connection of the interannual-to-decadal variation of 52 with the Pacific Decadal Oscillation (PDO) and the ultimate global-change processes that cause PDO. More work is underway, and additional independent data sources are examined, paying close attention to the fact that the J2 anomaly has been reversing back to normal since 2001. These include: (1) cryospheric contributions (melting of glaciers and ice sheets); (2) land hydrological contributions; (3) polar sea influences ( e g , via deep flow); (4) fluid flow in Earth's core; (5) time-variable gravity signals from SLR in higher harmonic degree/order, including J3,J4, (2,1), and (2,2) coefficients, considering their lower signal-to-noise ratios; (6) Earth rotation data in terms of length-of-day and polar motion.

Chao, B. F.↗

NASA NTRS2011

Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

Li, Bailing↗

NASA NTRS2017

Status and Plans for Reanalysis at NASA/GMAO

Reanalysis plays a critical role in GMAOs goal to enhance NASA's program of Earth observations, providing vital data sets for climate research and the development of future missions. As the breadth of NASAs observations expands to include multiple components of the Earth system, so does the need to assimilate observations from currently uncoupled components of the system in a more physically consistent manner. GMAOs most recent reanalysis of the satellite era, MERRA-2, has completed the period 1980-present, and is now running as a continuing global climate analysis with two- to three-week latency. MERRA-2 assimilates meteorological and aerosol observations as a weakly coupled assimilation system as a first step toward GMAOs longer term goal of developing an integrated Earth system analysis (IESA) capability that will couple assimilation systems for the atmosphere, ocean, land and chemistry. The GMAO strategy is to progress incrementally toward an IESA through an evolving combination of coupled systems and offline component reanalyses driven by, for example, MERRA-2 atmospheric forcing. Most recently, the GMAO has implemented a weakly coupled assimilation scheme for analyzing ocean skin temperature within the existing atmospheric analysis. The scheme uses background fields from a near-surface ocean diurnal layer model to assimilate surface-sensitive radiances plus in-situ observations along with all other observations in the atmospheric assimilation system. In addition, MERRA-2-driven simulations of the ocean (plus sea ice) and atmospheric chemistry (for the EOS period) are currently underway, as is the development of a coupled atmosphere-ocean assimilation system. This talk will describe the status of these ongoing efforts and the planned steps toward an IESA capability for climate research.

MERRA-2↗

NASA NTRS2011

Space-Based Sensorweb Monitoring of Wildfires in Thailand

We describe efforts to apply sensorweb technologies to the monitoring of forest fires in Thailand. In this approach, satellite data and ground reports are assimilated to assess the current state of the forest system in terms of forest fire risk, active fires, and likely progression of fires and smoke plumes. This current and projected assessment can then be used to actively direct sensors and assets to best acquire further information. This process operates continually with new data updating models of fire activity leading to further sensing and updating of models. As the fire activity is tracked, products such as active fire maps, burn scar severity maps, and alerts are automatically delivered to relevant parties.We describe the current state of the Thailand Fire Sensorweb which utilizes the MODIS-based FIRMS system to track active fires and trigger Earth Observing One / Advanced Land Imager to acquire imagery and produce active fire maps, burn scar severity maps, and alerts. We describe ongoing work to integrate additional sensor sources and generate additional products.

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