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DOE OSTI2023

Multi-layered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2018

Atmosphere-Ocean Coupled Data Assimilation Using NASA GEOS: Estimation of Air-Sea Interface State Variables

Air-sea interface variables, such as the skin Sea Surface Temperature (SST) are essential for atmosphere-ocean coupling. In the NASA GMAO Data Assimilation System (DAS), the skin SST and 3-D atmospheric state are jointly estimated [1]. This presentation is focused on the prior or background error covariance that is used in this analysis. The GEOS DAS uses an ensemble-variational assimilation strategy. In that, specification of a climatological background (CB) error covariance for SST relies on the NOAA's OI SST, with estimates of standard deviation and correlation length scales based on weekly analyses of the bulk SST at 1 degree resolution. However, present analysis system is striving to resolve SST diurnal variability with six hourly analyses and assimilates a vast number of in-situ and satellite observations. The first part of this presentation re-derives the CB error covariance using OSTIA SST analyses and illustrates the impact of this update on assimilating satellite observations. In a hybrid assimilation system the CB error covariances are appended with a flow-dependent background error covariance estimate implied by the underlying ensemble. The second part of this presentation refers to: a. treatment of the skin SST in the ensemble members, b. corresponding ensemble spread, and c. impact of these additions on the data assimilation system. [1] S. Akella, et al. (2017), doi:10.1002/qj.2988

Akella, Santha↗

NASA NTRS2017

Meteorological Inventory of Rain-On-Snow Events in the Canadian Arctic Archipelago and Satellite Detection Assessment Using Passive Microwave Data

The spatial and temporal distributions of rain-on-snow (ROS) events across the Canadian Arctic Archipelago (CAA) remain poorly understood owing to their sporadic nature in time and space. This situation motivated the development of remote sensing detection algorithms. This paper uses a large meteorological dataset across the CAA to adapt an existing ROS-detection algorithm developed in a previous study by our group. Results highlight the spatial distribution and evolution of ROS occurrences reported since 1985 at 14 weather stations across the CAA. Results show that >600 ROS events were inventoried since 1985, for which >70% were classified as pure rain (liquid form) and 30% as mixed precipitation (solid/liquid). Of the pure rain events, 75% occurred during spring, 14% during fall, 8% during summer and <1% during winter. Such events can have significant impacts on ungulate grazing conditions through the creation of ice layers, causing serious problems for caribou calf survival, especially during the migration period. This paper introduces an adaptation for larger scale Arctic application of a detection algorithm (sensitivity analysis on the detection threshold) with an error of ~5%. The validation, however, remains limited due to a short study period and limited number of sites.

Canadian Arctic Archipelago (CAA)↗

DOE OSTI2020

Probabilistic Look-ahead Contingency Analysis Integration with Commercial Tool and Practical Data

This paper presents an initial effort of integrating a smart sampling-based probabilistic look-ahead contingency analysis algorithm with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool as a proof-of-concept for a seamless research tool integration using real world large-scale grid data. With the increasing impact of random forces such as variable generation and load, their stochastic behaviors cannot be ignored. However, the current practices are still dominated by deterministic tools. They are becoming increasingly inadequate for the future grid. The developed look-ahead contingency analysis algorithm incorporates forecast errors of variable energy and load to address the challenges brought by the increasing uncertainty of power system. The algorithm can reveal the potential violations caused by the variance of variable energy and load that are not normally detected by traditional deterministic approaches. To test its performance under practical environments ( real data with real commercial tool), significant efforts have been made to prepare test cases, modify GE EMS tool, and adapt an extreme value distribution algorithm to analyze the GE EMS’s violation-only outputs. The test results clearly demonstrate the effectiveness of the developed algorithm as new transformer violations that were not previously detected have been identified. This performance provides better situational awareness to engineers for their decision-making process under uncertainty. Moreover, with the discussion of computational performance and future work, this paper has shown a clear path for integrating the probabilistic algorithm with commercial tools to make us better equipped for the changing power system.

Modeling and simulation of power systems, constrai↗

NASA NTRS2018

Assessment and Verification of SLS Block 1-B Exploration Upper Stage State and Stage Disposal Performance

One of the SLS Navigation System's key performance requirements is a constraint on the payload system's delta-v allocation to correct for insertion errors due to vehicle state uncertainty at payload separation. The SLS navigation team has developed a Delta-Delta-V analysis approach to assess the effect on trajectory correction maneuver (TCM) design needed to correct for navigation errors. This approach differs from traditional covariance analysis based methods and makes no assumptions with regard to the propagation of the state dynamics. This allows for consideration of non-linearity in the propagation of state uncertainties. The Delta-Delta-V analysis approach re-optimizes perturbed SLS mission trajectories by varying key mission states in accordance with an assumed state error. The state error is developed from detailed vehicle 6-DOF Monte Carlo analysis or generated using covariance analysis. These perturbed trajectories are compared to a nominal trajectory to determine necessary TCM design. To implement this analysis approach, a tool set was developed which combines the functionality of a 3-DOF trajectory optimization tool, Copernicus, and a detailed 6-DOF vehicle simulation tool, Marshall Aerospace Vehicle Representation in C (MAVERIC). In addition to delta-v allocation constraints on SLS navigation performance, SLS mission requirement dictate successful upper stage disposal. Due to engine and propellant constraints, the SLS Exploration Upper Stage (EUS) must dispose into heliocentric space by means of a lunar fly-by maneuver. As with payload delta-v allocation, upper stage disposal maneuvers must place the EUS on a trajectory that maximizes the probability of achieving a heliocentric orbit post Lunar fly-by considering all sources of vehicle state uncertainty prior to the maneuver. To ensure disposal, the SLS navigation team has developed an analysis approach to derive optimal disposal guidance targets. This approach maximizes the state error covariance prior to the maneuver to develop and re-optimize a nominal disposal maneuver (DM) target that, if achieved, would maximize the potential for successful upper stage disposal. For EUS disposal analysis, a set of two tools was developed. The first considers only the nominal pre-disposal maneuver state, vehicle constraints, and an a priori estimate of the state error covariance. In the analysis, the optimal nominal disposal target is determined. This is performed by re-formulating the trajectory optimization to consider constraints on the eigenvectors of the error ellipse applied to the nominal trajectory. A bisection search methodology is implemented in the tool to refine these dispersions resulting in the maximum dispersion feasible for successful disposal via lunar fly-by. Success is defined based on the probability that the vehicle will not impact the lunar surface and will achieve a characteristic energy (C3) relative to the Earth such that it is no longer in the Earth-Moon system. The second tool propagates post-disposal maneuver states to determine the success of disposal for provided trajectory achieved states. This is performed using the optimized nominal target within the 6-DOF vehicle simulation. This paper will discuss the application of the Delta-Delta-V analysis approach for performance evaluation as well as trajectory re-optimization so as to demonstrate the system's capability in meeting performance constraints. Additionally, further discussion of the implementation of assessing disposal analysis will be provided.

Patrick, Sean↗

DOE OSTI2024

The right conditions for high-precision dynamic temperature and heat capacity measurement via pyrometry and conductivity

The pursuit of accurate bulk temperature T under extreme conditions has been a long-standing goal of the high pressure science community, complicated by a lack of data to inform models. To reach these extremely high-pressure, high-temperature (high P − T) conditions, a combination of dynamic and heated static experiments (e.g., diamond or gem anvil cel experiments) are used. For example, in a diamond anvil cell (DAC) experiment, a sample placed in the DAC is first pressurized. Following pressurization, the sample T is increased either by heating the entire DAC (usually using resistive heating, and limited to ∼1000K) or by applying intense laser power to the sample surfaces. In a dynamic experiment, the process of pressurizing the sample also heats it. In the case of shock physics experiments, such heating is substantial, easily reaching thousands of Kelvin; in our work we have seen T ∼17000K. Most methods of measuring temperature at ambient are not compatible with experiments under these high-pressure, high-temperature conditions: thermocouples break, melt, or have conductivity properties that differ from ambient where they are calibrated; thermometers would melt; both are too slow. As a result most methods are based on non-contact techniques such as x-ray diffraction broadening, neutron scattering, or optical methods. Of these, optical methods using the visible and near-infrared region of the spectrum are the most commonly used as the sources and detectors are readily available. In the case of optical methods the optical depth, and therefore the measurement location, is limited to the surface. When a window or anvil material is used, heat flows from the sample into the window/anvil. Likewise, if the sample undergoes a change in thermodynamic state, such as expansion upon release, different T may be expected. As a result, the surface or apparent temperature T app measurement will differ from the bulk or interior temperature that is desired. This surface measurement must be related to the bulk measurement using thermal transport models and material models. While it is tempting to conclude that one should just use x-ray methods that directly probe the interior, even these methods have been shown to depend on thermal transport and material models. Regardless of the method used to create the high P − T condition, therefore, we must understand the role of thermal transport and material models upon our interpretation of the T measurement, as well as the errors and uncertainties associated with the choice of models used in the analysis. This is a substantial area of research and this paper is by no means a complete survey of the relevant sources of uncertainty. For example, we have yet to begin to address alternate transport models in a detailed manner (e.g., Tan-Ahrens), or the many models that use additional layers to approximate melting, turbulence, or epitaxial phenomena). Likewise, we have not explored the impact upon uncertainty of thermal models that use temperature-dependent thermal transport coefficients, or the wide range of material models that can be applied. Instead, this paper focuses on using one simple model, the Urtiew-Grover model, to understand the sources of error in T measurement so that we may identify how best to focus future research efforts to return the best improvements and avoid working on over-optimizing a single type of measurement. To this end, we work through some of the best and worst case scenarios for T measurement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems

We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Ly α systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel -1 . Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N > 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1975

Ballistic intercept missions to Comet Encke

The optimum ballistic intercept of a spacecraft with the comet Encke is determined. The following factors are considered in the analysis: energy requirements, encounter conditions, targeting error, comet activity, spacecraft engineering requirements and restraints, communications, and scientific return of the mission. A baseline model is formulated which includes the basic elements necessary to estimate the scientific return for the different missions considered. Tradeoffs which have major impact on the cost and/or scientific return of a ballistic mission to comet Encke are identified and discussed. Recommendations are included.

Mumma, M.↗

NASA NTRS2011

Terminal - Tactical Separation Assured Flight Environment (T-TSafe)

The Tactical Separation Assured Flight Environment (TSAFE) has been previously tested as a conflict detection and resolution tool in the en-route phase of flight. Fast time simulations of a terminal version of this tool called Terminal TSAFE (T-TSAFE) have shown promise over the current conflict detection tools. It has shown to have fewer false alerts (as low as 2 per hour) and better prediction to conflict time than Conflict Alert. The tool will be tested in the simulated terminal area of Los Angeles International Airport, in a Human-in-the-loop experiment to identify controller procedures and information requirements. The simulation will include comparisons of T-TSAFE with NASA's version of Conflict Alert. Also, some other variables such as altitude entry by the controller, which improve T-TSAFE's predictions for conflict detection, will be tested. T-TSAFE integrates features of current conflict detection tools such as Automated Terminal Proximity Alert used to alleviate compression errors in the final approach phase. Based on fast-time simulation analysis, the anticipated benefits of T-TSAFE over Conflict Alert include reduced false/missed alerts and increased time to predicted loss of separation. Other metrics that will be used to evaluate the tool's impact on the controller include controller intervention, workload, and situation awareness.

Verma, Savita Arora↗

NASA NTRS2020

Impact of Bias in the Marine Air Temperature Observation Set on Atmospheric Reanalyses

Marine air temperature observations are collected by volunteer observing ships carrying automatic meteorological sensor packages. Close proximity of the package mount to a dark colored ship's deck is known to introduce a warm bias on sunny afternoons in summer under low wind conditions. This observation set is used as an influential constraint in the MERRA-2 reanalysis, as well as several other reanalyses, in the poorly sampled surface marine boundary layer. Here we explore the consequences of bias in the marine air temperature observation set during the 36 year period 1980-2015. Marine air temperature observations have mainly been collected along major shipping routes, with the densest coverage in the North Atlantic and North Pacific. In the first part of this presentation we review the historical observation coverage and compare air temperature and contemporaneous SST measurements to identify systematic differences. The comparison suggests the presence of diurnal bias in the air temperature observations peaking in subtropics and midlatitudes latitudes in mid-afternoon local time. In the second part we explore the impact of the marine air temperature observations on MERRA-2 through analysis of the terms in the temperature forecast tendency equation, the equation which describes the contributions to changes in temperature during each analysis cycle. Examination of the temperature forecast tendency equation allows us to separate systematic errors due to such processes and radiation, mixing, and advection and allows us to identify the major source of systematic forecast tendencies. The results show that the marine air temperature measurements introduce an error that can reach 0.4K/dy which also peaks in local afternoon at subtropical latitudes. This bias varies with season and has gradually reduced from the beginning of the MERRA-2 reanalysis in 1980. In the final part we conduct numerical experiments with a version of the MERRA-2 analysis system, comparing experiments when the marine air temperature observations are included or withheld from the analysis system.

Akella, Santharam↗

NASA NTRSDate not supplied

Evaluating GXS Impact in the Context of International Coordination

The proposed NOAA/NASA Geostationary eXtended Observations (GeoXO) program plans to include a hyperspectral infrared (IR) sounder on its central satellite. Expected to launch in the mid-2030s, the GeoXO Sounder (GXS) will join international counterparts in a geostationary orbit. Ahead of launch, the NASA Global Modeling and Assimilation Office (GMAO) assessed the potential effectiveness of GXS both as a single GEO IR sounder and as part of a global ring of such instruments, including those already being built by international agencies. Using an observing system simulation experiment (OSSE) framework, GXS was assessed from a global numerical weather prediction (NWP) perspective. The ability of GXS, both alone and as part of a global ring of GEO sounders, to improve weather prediction of thermodynamic variables was evaluated globally and regionally. Compared to a control, GXS dominated regional analysis and forecast improvements, and contributed significantly to global increases in forecast skill. However, more sustained global improvements on the order of 4 days rely on international partnerships. Using the FSOI metric over CONUS, the GXS observations provide the strongest radiance impact on the moist energy error norm reduction. Additionally, GXS shows the capability to improve hurricane forecast track errors, resulting in improved forecast warnings. Overall, the persistent atmospheric profile information from GXS over much of the western hemisphere provide an opportunity to improve the representation of weather systems and their forecasts.

Erica McGrath-Spangler↗

NASA NTRSDate not supplied

Evaluating the Impact of Geostationary Sounders in the Context of International Coordination

The proposed NOAA/NASA Geostationary eXtended Observations (GeoXO) program plans to include a hyperspectral infrared (IR) sounder (GXS) on its central satellite, joining international counterparts. Ahead of launch, the NASA Global Modeling and Assimilation Office (GMAO) assessed the potential effectiveness of GXS both as a lone sounder in a GEO orbit and as part of a global ring of such instruments. Using an observing system simulation experiment (OSSE) framework from a global numerical weather prediction (NWP) perspective, the ability of GXS and the global ring to improve weather prediction of thermodynamic variables was assessed both globally and regionally. GXS dominated regional analysis and forecast improvements and contributed significantly to global increases in forecast skill. However, more sustained global improvements on the order of 4 days rely on international partnerships. Over CONUS, the FSOI metric showed the GXS observations provide the strongest radiance impact on the moist energy error norm reduction. Additionally, GXS shows the capability to improve hurricane forecast track errors, resulting in improved forecast warnings. Overall, the persistent atmospheric profile information from GXS over much of the western hemisphere provide an opportunity to improve the representation of weather systems and their forecasts.

Erica L. McGrath-Spangler↗

NASA NTRSDate not supplied

Evaluating the Impact of Geostationary Sounders in the Context of International Coordination

Erica McGrath-Spangler↗

NASA NTRSDate not supplied

Evaluating GXS Impact in the Context of International Coordination

Erica McGrath-Spangler↗

NASA NTRS2018

Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit (AMSU-A), Microwave Humidity Sounder (MHS), and GMI on GEOS-5 analyses and forecasts of various hurricanes.

Kim, Min-Jeong↗

NASA NTRSDate not supplied

SPACEFLIGHT ASSOCIATED NEURO-OCULAR SYNDROME (SANS): 2023 CLINICAL UPDATE

INTRODUCTION Spaceflight Associated Neuro-ocular Syndrome (SANS) is a condition unique to long-duration spaceflight, with an unclear pathogenesis and pathophysiology, and no perfect terrestrial analog. Approximately 66% of long duration spaceflight (LDSF) astronauts present with the earliest indication of SANS, which is defined as development of any of the following signs in at least one eye during or immediately following spaceflight: 1) optic disc edema (ODE; represented by an increase of ≥ 20 microns in peripapillary total retinal thickness [ΔTRT]); 2) chorioretinal folds; 3) globe flattening; and 4) excessive shift in refractive error (≥ +0.75D). Each of these signs presents potential risk to a crewmember’s vision and mission effectiveness, with ODE posing the highest risk overall. It is not yet known what severity and/or duration of these signs might lead to acute or permanent impacts to ocular anatomy or visual performance. Brain anatomical changes also occur during long-duration spaceflight and are being monitored in the astronaut population; however, these changes have not yet been associated with functional decrements or with SANS. An update will be provided on the latest SANS clinical analyses, diagnostics, and program initiatives. METHODS Data were obtained from clinical records and SANS subject matter experts (SMEs) to compile a SANS clinical update. Areas of interest include: 1) prevalence of SANS signs, 2) defining clinical thresholds for SANS, and 3) an overview of current and planned SANS clinical efforts. DISCUSSION SANS Sign Prevalence: Analysis at the time of this abstract submission indicates that the approximate prevalence of primary SANS findings in USOS LDSF crewmembers is: 64% for ODE (≥ 20-micron ΔTRT), 15% for chorioretinal folds, 26% for globe flattening, and 14% for hyperopic shifts in refractive error (≥ +0.75D). Clinical Thresholds:  The Earliest Indication of SANS was presented publicly for the first time at the 2020 NASA Human Research Program Investigators’ Workshop. See definition in Introduction section (above).  Clinically Concerning SANS – Development of any of the following signs during or immediately following spaceflight: 1) ODE (≥ 55-micron ΔTRT* and/or Frisèn grade ≥1*), 2) sharp chorioretinal folds in the vicinity of the macula, or 3) moderate globe flattening.  Pathological SANS with an Acute Functional Impact – Development of any of the following signs/symptoms during or immediately following spaceflight: 1) visual field loss (e.g., enlarged blindspot), 2) distorted centralvision, or 3) shift in refractive error beyond power of available correction (e.g., “space anticipation glasses”).  Pathological SANS affecting Long-Term Health – Development of any of the following signs/symptoms during or following spaceflight: 1) permanent visual field loss, 2) reduced retinal nerve fiber layer (RNFL) thickness, 3) permanently distorted central vision, 4) atrophy of retinal pigment epithelium (RPE) or photoreceptors, or 5) choroidal neovascularization. Current & Planned Clinical Efforts (accurate at time of abstract submission): All SANS diagnostic hardware are performing nominally onboard the International Space Station: vision screening, fundoscope, optical coherence tomography (OCT) device, ultrasound, and tonometer. The Goggle-Based Visual Field (GBVF) device is nearing completion of its clinical validation study at Ohio State University and is planned for parabolic flight testing in fiscal year 2023 (FY23); pending positive results, this sequence may pave the way towards an ISS technology demonstration in FY24.

T J Brunstetter↗

NASA NTRS2021

Asymmetry in Subseasonal Surface Air Temperature Forecast Error with Respect to Soil Moisture Initialization

Soil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state.

Air Temperature↗

NASA NTRSDate not supplied

Assessing the Impact of SMAP Soil Moisture Data Assimilation on the Simulation and Prediction of Tropical Cyclone Idai

The role of soil moisture in the evolution of tropical cyclones (TCs) approaching land has long been recognized. Dry land surface conditions can lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions can help sustain or even re-intensify a TC. The ability to forecast post-landfall TC evolution and mitigate the associated socio-economic impact thus hinges on accurate knowledge of land surface conditions prior to landfall. The NASA Soil Moisture Active Passive (SMAP) mission provides accurate observations of soil moisture globally and at high revisit times of 2-3 days. It has been shown that the assimilation of SMAPbrightness temperatures (Tb) significantly improves modeled land surface states and thus has the potential to constrain land surface initial conditions in TC forecasts. In this presentation, we investigate this potential through an extensive set of Observing System Experiments that systematically assess the impact of assimilating SMAP Tbs on TC forecast skill in the Goddard Earth Observing System. Focusing on the case of TC Idai, we show that the assimilation of SMAP generally leads to a drier soil moisture analysis for the land surface underneath much of the storm’s circulation. An exception is the direct TC track, where SMAP assimilation increases soil moisture. These changes are reflected in the surface fluxes, which propagate the land surface state changes to the atmosphere. This results a TC that is overall weaker with lower winds, but with a better-defined eye and a slightly smaller track error than a simulation without SMAP DA. The changes in soil moisture resulting from the assimilation of SMAP also impact the total precipitation amounts as well as the precipitation structure associated with TC Idai.

SMAP↗