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NASA NTRSDate not supplied

Spaceborne Lidar Retrievals of PM2.5 for Air Quality Studies and Applications

Fine particulate matter (PM2.5) substantially contributes to air pollution and negatively affects human health. While many studies have investigated the use of passive column-integrated aerosol optical depth to infer surface PM2.5, the use of lidar observations for air quality characterization is not nearly as extensive. Lidar measurements are critical, however, due to the vertical aerosol information they provide, including near the surface. In this presentation, we first provide an overview of various lidar-based approaches for estimating PM2.5 concentrations and then discuss how lidar measurements can assist other air quality applications. For example, estimates of PM2.5 have been obtained in a physics-based approach through CALIOP near-surface aerosol extinction retrievals, assumptions on the mass extinction efficiency, and incorporating other parameters (an aerosol hygroscopic growth factor and PM2.5/PM10 ratio). Application of this algorithm over the contiguous United States (CONUS) from 2006 to 2018 yielded larger PM2.5 values over the eastern and western CONUS (~10-15 μg/m³) and lower PM2.5 levels in the central CONUS (~5 μg/m³). These spatial patterns were similar to those from gridded PM2.5 concentrations obtained through in situ measurements at ground stations operated by the US Environmental Protection Agency. In another approach, the Cloud Aerosol Transport System (CATS) lidar was used with the Goddard Earth Observing System (GEOS) model in a 1D ensemble-based variational technique to obtain PM2.5 over the US and Europe, and the spatial patterns of the CATS/GEOS based PM2.5 concentrations generally captured those from surface stations (with corresponding hourly EPA PM2.5 vs CATS PM2.5 statistics of R=0.4 and bias=1.5 μg/m³). In our recent work, as part of the Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group, we have applied both the CALIOP and CATS/GEOS based approaches over the highly polluted country of India during the post-monsoon season (September-October 2016). We derived elevated levels of two-month mean PM2.5 (~100 μg/m³) in northern India, especially near New Delhi. These high PM2.5 concentrations in the Indo-Gangetic plain are driven in large part from the seasonal burning of crop residue and meteorological conditions typical at this time of the year, such as low wind speeds and a shallow boundary layer. While the satellite-derived PM2.5 moderately replicates (R = ~0.7-0.9) the spatial variability in the two-month mean of surface in situ PM2.5 from monitoring sites operated by the Central and State Pollution Control Boards, we show results from specific scenes for which there are large deviations between the satellite-derived PM2.5 and in situ measurements. Other current work on this topic focuses on developing PM2.5 estimates using airborne high spectral resolution lidar measurements through machine learning regression algorithms and involves several parameters (e.g., aerosol extinction, color ratio, lidar ratio). Application of this method over major metropolitan areas in the US and Asia have resulted in high correlations (R = 0.93) with surface measurements. This airborne lidar approach can be adapted to spaceborne lidar measurements, and all three of these approaches can be applied to ESA’s EarthCARE Atmospheric Lidar instrument, setting the stage for the future Cloud Aerosol Lidar for Global Scale Observations of the Ocean-Land Atmosphere System (CALIGOLA) mission. Ultimately, beyond estimates of PM2.5, the aerosol vertical distribution from lidars can benefit studies involving passive sensor approaches for PM2.5 proxies (including from geostationary satellites), wildfire smoke plume injection heights, volcanic emissions (e.g., ash height retrievals), and aerosol/air quality model assimilation, evaluation, and forecasts.

Travis D Toth↗

NASA NTRSDate not supplied

Satellite-Assisted Particulate Matter (SAPM) for the Models, In situ, and Remote Sensing of Aerosols (MIRA) Working Group

Models, In situ, and Remote sensing of Aerosols (MIRA) is an international working group that provides a forum for collaborations amongst these three atmospheric aerosol communities. MIRA consists of a collection of interdisciplinary and independently funded Topic/Project groups with clear goals and generally characterized by requests for additional scientific data. The Satellite-Assisted Particulate Matter (SAPM) Topic group focuses on the study of fine particulate matter (PM2.5), as it is a major contributor to air pollution and negatively impacts human health. Our group aims to provide intercomparisons of various methods and techniques for estimating surface PM2.5 assisted by satellite remote sensors (passive and active), global aerosol models, and in situ aerosol measurements. The overall motivation of our work is to enhance PM2.5 coverage beyond in situ ground stations, which can be limited. In this presentation, we provide an overview of various PM2.5 estimation approaches by current SAPM team members, as each approach has its own strengths and limitations. One approach uses near-surface aerosol extinction retrievals from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and an assumed value of mass extinction efficiency to derive PM2.5 concentrations (Toth et al 2019). A study using this method found a promising agreement (R=0.60; slope=0.89) with U.S. Environmental Protection Agency (EPA) in situ PM2.5 measurements over the contiguous United States (CONUS) for a 12-year (2007-2018) period (Toth et al 2022). In a different approach combining spaceborne lidar and a model, the Cloud Aerosol Transport System (CATS) lidar and Goddard Earth Observing System (GEOS) model are blended in an ensemble variational assimilation scheme to retrieve aerosol extinction and convert speciated mass concentrations to total PM2.5. A recent study (Matus et al in prep.) found agreement within 2 μg/m3 over the CONUS on the 2016 annual mean when compared to EPA ground-based measurements. Another study explored trends in city aerosol optical depth (AOD) from a spaceborne passive remote sensor, Moderate Resolution Imaging Spectroradiometer (MODIS), and found that these trends agree well with trends in city surface PM2.5, which provides confidence in the use of city AOD for city PM2.5 trend studies (Vohra et al 2021). In a follow-on study, steep and significant trends (~2.5 to 7.8% a-1) in AOD are found for several South Asian cities (e.g., Bangalore and Hyderabad), suggesting rapid growth in PM2.5 (Vohra et al 2022). Also in this presentation, we report results on our current collective SAPM study of India, a country that exhibits high levels of PM2.5 pollution. We show the temporal (i.e., seasonal) and spatial variability of PM2.5 over India using the CALIOP and CATS+model approaches, comparisons between the estimated PM2.5 from the retrieval schemes, and validation with in situ PM2.5 ground-based observations from the Central Pollution Control Board in India. This region provides us with an excellent case study to test our PM2.5 retrievals in heavily polluted scenes and over a complex and varying topography. The SAPM Topic group actively seeks international participants/collaborators in our group, including those working with in situ aerosol measurements (e.g., ACTRIS). We are interested in aerosol datasets in order to either improve our PM2.5 estimates from various approaches (e.g., using in situ mass scattering/absorption coefficient and aerosol hygroscopic properties for various aerosol species) and/or validate the PM2.5 estimates (e.g., using in situ ground-based PM2.5 concentrations).

Travis D Toth↗

NASA NTRSDate not supplied

Variational Coupled Loads Analysis using the Hybrid Parametric Variation Method

Time-domain coupled loads analysis (CLA)is used to determine the response of a launch vehicle and payload system to transient forces, such as liftoff, engine ignitions and shutdowns, jettison events, and atmospheric flight loads, such as buffet. CLA, using Hurty/Craig-Bampton (HCB)component models, is the accepted method for the establishment of design-level loads for launch systems. However, uncertainty in the component models flows into uncertainty in predicted system results. Uncertainty in the structural responses during launch is a significant concern because small variations in launch vehicle and payload mode shapes and their interactions can result in significant variations in system loads. Uncertainty quantification (UQ)is used to determine statistical bounds on prediction accuracy based on model uncertainty. In this paper uncertainty is treated at the HCB component-model level. In an effort to account for model uncertainties and statistically bound their effect on CLA predictions, this work combines CLA with UQ in a process termed variational coupled loads analysis (VCLA). The modeling of uncertainty using a parametric approach, in which input parameters are represented by random variables, is common, but its major drawback is the resulting uncertainty is limited to the form of the nominal model. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. Model-form uncertainty can be represented using a nonparametric approach based on random matrix theory (RMT). In this work, UQ is performed using the hybrid parametric variation (HPV)method, which combines parametric with nonparametric uncertainty at the HCB component model level. The HPV method requires the selection of dispersion values for the HCB fixed-interface (FI)eigenvalues, and the HCB mass and stiffness matrices. The dispersions are based upon component test-analysis modal correlation results. During VCLA, random component models are assembled into an ensemble of random systems using a Monte Carlo (MC)approach. CLA is applied to each of the ensemble members to produce an ensemble of system-level responses for statistical analysis. The proposed methodology is demonstrated through its application to a buffet loads analysis of NASA’s Space Launch System (SLS)during the transonic regime fifty seconds after liftoff. Core stage (CS)section shears and moments are recovered, and statistics are computed.

Uncertainty Quantification↗

NASA NTRS2013

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

NOAA CPC↗

NASA NTRS2012

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Reichle, Rolf H.↗

NASA NTRS2003

Simulated KWAJEX Convective Systems Using a 2D and 3D Cloud Resolving Model and Their Comparisons with Radar Observations

The 1999 Kwajalein Atoll field experiment (KWAJEX), one of several major TRMM (Tropical Rainfall Measuring Mission) field experiments, has successfully obtained a wealth of information and observation data on tropical convective systems over the western Central Pacific region. In this paper, clouds and convective systems that developed during three active periods (Aug 7-12, Aug 17-21, and Aug 29-Sep 13) around Kwajalein Atoll site are simulated using both 2D and 3D Goddard Cumulus Ensemble (GCE) models. Based on numerical results, the clouds and cloud systems are generally unorganized and short lived. These features are validated by radar observations that support the model results. Both the 2D and 3D simulated rainfall amounts and their stratiform contribution as well as the heat, water vapor, and moist static energy budgets are examined for the three convective episodes. Rainfall amounts are quantitatively similar between the two simulations, but the stratiform contribution is considerably larger in the 2D simulation. Regardless of dimension, fo all three cases, the large-scale forcing and net condensation are the two major physical processes that account for the evolution of the budgets with surface latent heat flux and net radiation solar and long-wave radiation)being secondary processes. Quantitative budget differences between 2D and 3D as well as between various episodes will be detailed.Morover, simulated radar signatures and Q1/Q2 fields from the three simulations are compared to each other and with radar and sounding observations.

Shie, Chung-Lin↗

NASA NTRS1995

Cloud model-based simulation of spaceborne radar observations

Simulations of observations from potential spaceborne radars are made based on storm structure generated from the three-dimensional (3D) Goddard cumulus ensemble model simulation of an intense overland convective system. Five frequencies of 3, 10, 14, 35, and 95 GHz are discussed, but the Tropical Rainfall Measuring Mission precipitation radar sensor frequency (14 GHz) is the focus of this study. Radar reflectives and their attenuation in various atmospheric conditions are studied in this simulation. With the attenuation from cloud and precipitation in the estimation of reflectivity factor (dBZ), the reflectivities in the lower atmosphere in the convective cores are significantly reduced. With spatial resolution of 4 km X 4 km, attenuation at 14 GHz may cause as large as a 20-dBZ difference between the simulated measurements of the peak, Z(sub mp) and near-surface reflectivity, Z(sub ms) in the most intense convective region. The Z(sub mp) occurs at various altitudes depending on the hydrometeor concentrations and their vertical distribution. Despite the significant attenuation in the intense cores, the presence of the rain maximum is easily detected by using information of Z(sub mp). In the stratiform region, the attenuation is quite limited (usually less than 5 dBZ), and the reduction of reflectivity is mostly related to the actual vertical structure of cloud distribution. Since Z(sub ms) suffers severe attenuation and tends to underestimate surface rainfall intensity in convective regions. Z(sub mp) can be more representative for rainfall retrieval in the lower atmosphere in these regions. In the stratiform region where attenuation is negligible, however, Z(sub mp) tends to overestimate surface rainfall and Z(sub ms) is more appropriate for rainfall retrieval. A hybrid technique using a weight between the two rain intensities is tested and found potentially usefull for future applications. The estimated surface rain-rate map based on this hybrid approach captures many of the details of the cloud model rain field but still slightly underestimates the rain-rate maximum.

Yeh, H.-Y. M.↗

NASA NTRS1998

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Feature Map

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a Self Organizing Feature Map (SOFM). Local linear modeling of nonlinear autonomous systems with the SOFM is extended to a control framework where the modeled system is nonautonomous, driven by an exogenous input. This extension to a control framework is based on the consideration of a finite number of subregions in the control space. Multiple self organizing feature maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experimental knowledge gained from decades of operation. Each SOFM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOFM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal. Each SOFM provides a codebook representation of the tunnel dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the minimization of a similarity metric which is the essence of the self organizing feature of the map. Thus, the SOFM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme than selects the best available model for the applied control. Experimental results of controlling the wind tunnel, with the proposed method, during operational runs where strict research requirements on the control of the Mach number were met, are presented. Comparison to similar runs under the same conditions with the tunnel controlled by either the existing controller or an expert operator indicate the superiority of the method.

Motter, Mark A.↗

NASA NTRS1999

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Map

Mark A. Motter↗

NASA NTRS2010

Investigation of Long-Term Impacts of Urbanization when Considering Global Warming for a Coastal Tropical Region

The overachieving goal of this project is to gain a better understanding of the climate impacts caused by the combined effects of land cover and land use (LCLU) changes and increasing global concentrations of green house gases (GHG) in tropical coastal areas, regions where global, regional and local climate phenomena converge, taking as the test case the densely populated northeast region of the Caribbean island of Puerto Rico. The research uses an integrated approach of high-resolution remote sensing information linked to a high resolution Regional Atmospheric Modeling System (RAMS), which was employed to perform ensembles of climate simulations (combining 2-LCLU and 2-GHG concentration scenarios). Reconstructed agricultural maps are used to define past LCLU, and combined with reconstructed sea surface temperatures (SST) for the same period form the PAST climate scenario (1951-1956); while the PRESENT scenario (2000-2004) was additionally supported by high resolution remote sensing data (10-m-res). The climate reconstruction approach is validated with available observed climate data from surface weather stations for both periods of time simulated. The selection of the past and present climate scenarios considers large-scale biases (i.e. ENSO/NAO) as reflected in the region of interest. Direct and cross comparison of the results is allowing quantifying single, combined, and competitive effects. Results indicate that global GHG have dominant effects on minimum temperatures (following regional tendencies), while urban sprawl dominates maximum temperatures. To further investigate impacts of land use the Bowen Ratio and the thermal response number (TRN) are analyzed. The Bowen ratio indicates that forestation of past agricultural high areas have an overwhelmingly mitigation effect on increasing temperatures observed in different LCLU scenarios, but when abandoned agricultural lands are located in plains, the resulting shrub/grass lands produce higher surface temperatures. The TRN (J/m^2/degC) is a surface property defined as the ratio of the surface net radiation to the rate of change in surface temperature, expresses how those fluxes are reacting to radiant energy inputs. Natural vegetated surfaces have a greater TRN than urban and barren surfaces because the net radiation processed by them is mostly used for latent heat and thermal storage heat rather than sensible heat (heating the air). Significant changes in TRN were observed in the metropolitan area of San Juan for the two analyzed periods reflecting a reduction of this variable in the present from the past consistent with increasing in thermal mass, or intense urbanization.

Gonalez, Jorge E.↗

NASA NTRS2003

Simulated Radar Characteristics of LBA Convective Systems: Easterly and Westerly Regimes

The 3D Goddard Cumulus Ensemble (GCE) model was used to simulate convection that occurred during the TRMM LBA field experiment in Brazil. Convection in this region can be categorized into two different regimes. Low-level easterly flow results in moderate to high CAPE and a drier environment. Convection is more intense like that seen over continents. Low-level westerly flow results in low CAPE and a moist environment. Convection is weaker and more widespread characteristic of oceanic or monsoon-like systems. The GCE model has been used to study both regimes n order to provide cloud datasets that are representative of both environments in support of TRMM rainfall and heating algorithm development. Two different cases are analyzed: Jan 26, 1999, an eastely regime case, and Feb 23, 1999, a westerly regime case. The Jan 26 case is an organized squall line, while the Feb 23 case is less organized with only transient lines. Radar signatures, including CFADs, from the two simulated cases are compared to each other and with observations. The microphysical processes simulated in the model are also compared between the two cases.

Lang, Stephen E.↗

NASA NTRS1986

A study of the response of deep tropical clouds to mesoscale processes - Three-dimensional numerical experiments

A three-dimensional numerical cloud model is utilized to study the collective feedback effects of cloud systems on the large-scale environment. The characteristic features of the cloud ensemble model are described. The model is applied to a case of a well-defined ITCZ rainband over the eastern tropical Atlantic ocean. The location, number, and configuration of the clouds that formed in the model during the simulations are examined. The cloud heating and moistening, environmental wind shear, and vertical transports of momentum are analyzed. The collective feedbacks of cloud systems on the large-scale temperature, moisture, and horizontal momentum fields are computed after each simulation. The effect of the pressure gradient force on the v-momentum flux is investigated. It is observed that there is good correlation between the model simulations and the observed data.

Tao, Wei-Kuo↗

NASA NTRS2020

Scaling Observation Error for Optimal Assimilation of CCI SST Data into a Regional HYCOM EnOI System

South Africa currently possesses no operational ocean forecasting system for the purpose of predicting ocean state variables including temperature,salinity and velocity. Substantial initial efforts towards this goal have been made and resulted in a system using a regional Hybrid Coordinate Ocean Model (HYCOM) along with the Ensemble Optimal Interpolation (EnOI)assimilation scheme. Assimilating only sea surface temperature (SST) observations from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) product into the system resulted in a degraded forecast. Aiming to address this, Climate Change Initiative (CCI) SSTs are assimilated into the system in an effort to improve the forecast skill. Observation errors in the assimilated product are used in the EnOI to determine whether more confidence should be placed in the model or observations in producing the analysis, but overconfidence in observations can shock the model and result in failure. To tweak the impact of the assimilation, a scaling factor is applied in the assimilation code. A scaling factor of 25 was found to produce a favourable result with lowest mean root mean square error (RMSE;1.098°C) between the model and observations over time. Postulating the error to be overconfident, a floor value is introduced in order to set a minimum value for the observation error thereby reducing confidence in the observations. These experiments fared less favourably with a floor value of 0.5 and a scaling factor of 15 producing the best mean RMSE (1.118°C).

Luyt, Hermann↗

NASA NTRSDate not supplied

On the Prediction of Surface Melt Over the Greenland Ice Sheet at NWP and S2S Timescales

Greenland surface melt “events” – encompassing large swaths of the ice sheet over approximately 4-10 day periods – have occurred with increasing frequency in the 21st Century. Surface processes are significant to the ice sheet’s overall contribution to sea level. In addition, the sudden onset of widespread surface melt produces hazardous conditions for researchers and for communities adjacent to the ice sheet. The mechanisms producing melt events, their associated large-scale forcing, and timescales of interest are highly variable and poorly understood. Here we examine the predictability of melt events in a numerical weather prediction system (NWP) and a subseasonal-to-seasonal system (S2S). The NASA Global Modeling and Assimilation Office forward processing (GMAO-FP) is an NWP system that incorporates an evolving, global hybrid 4D ensemble–variational (4DEnVar) data assimilation system and a state-of-the-art model that is run twice-daily with forecasts out to five and ten days. The GMAO S2S system is a hindcast-anomaly, coupled-model prediction system initialized with an atmospheric analysis, and in situ and satellite ocean data assimilation. Both systems represent ice sheet surface hydrology and snowpack processes. We examine the performance of the systems over the period 2018-2023, a span that includes approximately nine significant events, against analyses and satellite-derived melt extent. In addition to extent, we examine related conditions and their role in the melt forecast, including the onset of both omega and Rex atmospheric blocking patterns, sea surface temperatures, sea ice extent, water vapor transport, precipitation, and surface preconditioning. We further examine the tendency for false-alarm events in each system and assess reasons for the forecast performance. North Atlantic atmospheric blocking patterns leading to surface melt are well forecast by the NWP system out to around eight days, while the S2S system generally underestimates the magnitude of such events at monthly lead times. These systems suggest that while short term forecasts of ice sheet melt and melt events are relatively robust, S2S predictions of surface conditions are highly dependent on the ability of a coupled system to generate the short-term atmospheric conditions associated with surface melt events.

Richard Cullather↗

NASA NTRS2002

Mesoscale Convective Systems During SCSMEX: Simulations with a Regional Climate Model and a Cloud-Resolving Model

The South China Sea Monsoon Experiment (SCSMEX) was conducted in May-June 1998. One of its major objectives is to better understand the key physical processes for the onset and evolution of the summer monsoon over Southeast Asia and southern China (Lau et al. 2000). Multiple observation platforms (e.g., soundings, Doppler radar, ships, wind seafarers, radiometers, etc.) during SCSMEX provided a first attempt at investigating the detailed characteristics of convection and circulation changes, associated with monsoons over the South China Sea region. SCSMEX also provided precipitation derived from atmospheric budgets (Johnson and Ciesielski 2002) and comparison to those obtained from the Tropical Rainfall Measuring Mission (TRMM). In this paper, a regional climate model and a cloud-resolving model are used to perform multi-day integrations to understand the precipitation processes associated with the summer monsoon over Southeast Asia and southern China. The regional climate model is used to understand the soil - precipitation interaction and feedback associated with a flood event that occurred in and around China's Atlantic River during SCSMEX. Sensitivity tests on various land surface models, cumulus parameterization schemes (CASE), sea surface temperature (SST) variations and midlatitude influences are also performed to understand the processes associated with the onset of the monsoon over the S. China Sea during SCSMEX. Cloud-resolving models (CRMs) use more sophisticated and physically realistic parameterizations of cloud microphysical processes with very fine spatial and temporal resolution. One of the major characteristics of CRMs is an explicit interaction between clouds, radiation and the land/ocean surface. It is for this reason that GEWEX (Global Energy and Water Cycle Experiment) has formed the GCSS (GEWEX Cloud System Study) expressly for the purpose of improving the representation of the moist processes in large-scale models using CRMs. The Goddard Cumulus Ensemble (GCE) model is a CRM and is used to simulate convective systems associated with the onset of the South China Sea monsoon in 1998. The BRUCE model includes the same land surface model, cloud physics, and radiation scheme used in the regional climate model. A comparison between the results from the GCE model and regional climate model is performed.

Tao, W. K.↗

NASA NTRS2006

A Coupled GCM-Cloud Resolving Modeling System to Study Precipitation Processes

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud resolving models (CRMs) agree with observations better than traditional single column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA Satellite and field campaign cloud related data sets can provide initial conditions as well as validation for both the MMF and CRMs. Also we have implemented a Land Information System (LIS that includes the CLM and NOAH land surface models into the MMF. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM) This modeling system has been applied and tested its performance for two different climate scenarios, El Nino (1998) and La Nina (1999). The coupled new modeling system produced more realistic propagation and intensity of tropical rainfall systems and intraseasonal oscillations, and diurnal variation of precipitation that are very difficult to forecast using even the state-of-the-art GCMs. In this talk I will present: (1) a brief review on GCE model and its applications on precipitation processes (both Microphysical and land processes) and (2) The Goddard MMF and the Major difference between two existing MMFs (CSU MMF and Goddard MMF) and preliminary results (the comparison with traditional GCMs).

Tao, Wei-Kuo↗

NASA NTRS2003

Project trades model for complex space missions

A Project Trades Model (PTM) is a collection of tools/simulations linked together to rapidly perform integrated system trade studies of performance, cost, risk, and mission effectiveness. An operating PTM captures the interactions between various targeted systems and subsystems through an exchange of computed variables of the constituent models. Selection and implementation of the order, method of interaction, model type, and envisioned operation of the ensemble of tools rpresents the key system engineering challenge of the approach. This paper describes an approach to building a PTM and using it to perform top-level system trades for a complex space mission. In particular, the PTM discussed here is for a future Mars mission involving a large rover.

web services↗

NASA NTRS2023

Evaluation of a Regional Crop Model Implementation for Sub-National Yield Assessments in Kenya

CONTEXT: Cropping system models can be used to both assess regional food security and to monitor and predict agricultural drought. Agriculture in Kenya is extremely important to both the economy and food security of the country. OBJECTIVE: This study evaluated a regional implementation of a widely used crop model, the Decision Support System for Agrotechnology Transfer (DSSAT), within a coupled modeling framework, the Regional Hydrologic Extremes Assessment System (RHEAS), over Kenya. The goal of this study was to assess the ability of RHEAS to simulate the annual variability of maize yields at the county level and evaluate the uncertainty inherent in the model and inputs. METHODS: The RHEAS system implements a stochastic ensemble approach to account for field scale variabilities in crop management practices and underlying soil and weather conditions. Satellite-derived datasets were used to evaluate the land surface component of the system and seasonally disaggregated yield for 5 years was used to assess the performance of the cropping system model. RESULTS AND CONCLUSIONS: The median correlation between RHEAS and satellite-derived soil moisture and evapotranspiration estimates were 0.78, and 0.51, respectively, indicating that the model is able to capture the key drivers of the hydrological budget. Overall, RHEAS simulated yearly yield variations with a median correlation of 0.7 with reported yields, with the best performance in the short rains season. However, across both seasons, the RHEAS model was positively biased on the order of ~1.6 MT/ha. The overall median unbiased RMSE was 0.66 MT/ha. The RHEAS system shows skill at simulating extreme departures in anomalies, and a majority of the time (62.5%) the reported yields fall within the interquartile range of the simulations. SIGNIFICANCE: One of the most important areas of improvement for the next generation of agricultural data and models is to better understand and communicate the inherent uncertainties. This is especially critical in data-limited regions. Here we present a modeling system and its implementation that begins to address these concerns. We demonstrate the ability to simulate broad trends in yields at the county level for sub-annual yields with skills that commensurate previous national/annual level studies.

Crop model↗