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NASA NTRS2010

Observing System Variations Effect on Reanalyses

Reanalyses integrate multitudes of satellite and conventional observations data assimilation and numerical weather prediction. The result is that many disparate observation platforms, discontinuous in space and time, lead to complete and consistent representations the state of the weather. The component also provides physical fields rarely or never observed. However, the numerical model bias is continuously being corrected by the observational analysis, and this bias changes as variations in the observations occur. NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) sensitivity to variations in the observing systems are explored. Specifically, we will evaluate the water budget and transport processes as they relate to the advent of SSM/I and AMSU-A radiance assimilation, and an additional case of radiosonde station that exhibits a dramatic shift in mean water states. The MERRA input observation data, now available online, is used to explore these variations.

Bosilovich, Michael G.↗

DOE OSTI2017

A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic WPR forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors, a generalized Gaussian mixture model is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.

17 WIND ENERGY↗

NASA NTRS2015

Transitioning NASA and NOAA Satellite Products, Modeling Data Assimilation Techniques, and Nowcasting Tools to Operations

No abstract available

research-to-operations↗

DOE OSTI2022

Development of a triple-moment ice-phase cloud microphysics scheme and its application to the Single Column Atmosphere Model

Parameterization of cloud microphysics is critical for accurate simulation of weather and climate, in which the characteristics of cloud particle spectrum inevitably further affect climate simulation by changing the cloud evolution and cloud radiation effects. The popular currently used double-moment cloud microphysics schemes in numerical models can predict the intercept (N 0 ) and slope (λ) parameters of cloud particle spectrum but cannot predict the spectral shape parameter (μ), which hinders accurate description of cloud physical processes in climate models. Therefore, in the present study, we built upon the ideas of previously developed triple-moment cloud microphysics scheme, considered radar reflectivity factor as the third predictor in addition to number and mass concentration, and introduced its related prediction equation to the Single Column Atmosphere Model Version 5.3 (SCAM5.3). Moreover, the relevant microphysical process formulas in the model were revised, and a triple-moment ice-cloud microphysics scheme was constructed to predict the μ of ice particle spectrum. Based on this model, a 29-day case of the Atmospheric Radiation Measurement Program in the summer of 1997 (ARM97) was simulated, and the differences in cloud fraction and radiation simulations between the double- and triple-moment schemes were analysed. The μ of ice particle spectrum predicted by the triple-moment scheme mainly ranged from 0 to 4; the peak value was around 2, and at least 85% of the μ values were greater than the default 0. Therefore, the μ=0 setting in the double-moment scheme is unreasonable. Moreover, the developed triple-moment ice-cloud microphysics scheme yielded a narrower ice particle spectrum, which was closer to the observation results of previous studies, than the double-moment cloud microphysics scheme (e.g., height=233 hPa). Furthermore, compared with the double-moment scheme, the triple-moment scheme achieved closer simulations of the cloud fraction observations, particularly for ice clouds in the upper levels. Considering the close association between cloud fraction and radiation, the error with the triple-moment scheme was smaller than that with the double-moment scheme regardless of the shortwave (surface downward shortwave radiation, net downward shortwave radiation at the top of the atmosphere, and net shortwave radiation at the surface) or longwave (surface downward longwave radiation and net longwave radiation at the surface) flux density. Finally, the improvement mechanism of the triple-moment scheme on cloud fraction and radiation simulations was explored. The major reason is that the triple-moment scheme weakens the autoconversion of ice crystal to form snow and enhances the growth process of ice crystal deposition, thereby increasing the mass concentration of ice crystals and ice cloud fraction. Overall, the developed triple-moment ice-cloud microphysics scheme can help improve the simulation ability of models for cloud-climate feedback and other processes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1977

Data simulation studies for field programs, satellite system tests, and regional weather prediction

In the present paper, the status of the Drexel/NCAR regional numerical weather forecast system is summarized to show the extent of our experience and our immediate requirements for data within the United States on scales smaller than rawinsonde spacing (approximately 300 km). Preliminary experiments are described, and data assimilation problems are examined. It is shown that data requirements, assimilation methods, and forecast sensitivity differ for regional and large-scale models. Forecast verification statistics suggest that middle-troposphere temperature is well known but relative humidity is not. In some cases, regional forecasts require much better humidity data than available from rawinsondes. Numerics, lateral boundary conditions, model physics, and computing power are adequate for near real-time experimental regional prediction with horizontal mesh sizes down to 35 km. Subsynoptic data and assimilation are the primary challenges facing regional NWP.

Kreitzberg, C. W.↗

NASA NTRS1981

Applications of estimation theory to numerical weather prediction

Numerical weather prediction (NWP) is an initial value problem for a system of nonlinear partial differential equations in which the initial values are known only incompletely and inaccurately. Data at initial time can be supplemented, however, by observations of the system distributed over a time interval preceding it. Estimation theory was successful in approaching such problems for models governed by systems of ordinary differential equations and of linear PDEs. Estimation-theoretic methods for NWP are developed. A model exhibiting many features of large scale atmospheric flow important in NWP is the one governed by the shallow fluid equations. The estimation problem for a linearized formulation of these equations is studied. A finite difference version of the equations is used as a forecast model to simulate the numerical models used in NWP.

Cohn, S.↗

NASA NTRSDate not supplied

Tropical Convection Through the Lens of the INCUS Mission

The overarching goal of the recently selected NASA INvestigation of Convective UpdraftS (INCUS) mission is to enhance our understanding of why, when and where tropical convective storms form, and why only some of these storms produce extreme weather. Convective storms provide an important pathway for the transport of air and water between Earth’s surface and the upper troposphere. This vertical transport of air and water, often referred to as convective mass flux (CMF), plays a critical role in Earth’s weather and climate system through its impacts on microphysical and precipitation rates, detrainment and upper tropospheric moistening, high cloud feedbacks, and the large-scale circulation. Potential changes to the CMF as a function of the local environment or with changing climates may also have significant implications for severe weather such as flood-producing rainfall, damaging hail and lightning. In spite of the critical role of this vertical transport of water and air, representation of CMF remains a major source of error in weather and climate models, thereby limiting our ability to accurately predict convective storms and their impacts. The tropics-wide observations from INCUS will enhance our understanding of tropical convective storm processes and will provide guidance for representing these processes in numerical models across scales. INCUS is comprised of three SmallSat platforms each carrying a RainCube-heritage Ka-band 7-beam scanning radar. The satellite platforms will be 30 and 90 seconds apart, thus providing three time intervals (30, 90 and 120 seconds) over which observations will be made. INCUS will investigate CMF using a novel time-differenced radar reflectivity profile approach. In addition to the Ka-band radar, a single TEMPEST-D-heritage cross-track-scanning passive microwave radiometer will be housed on the middle SmallSat. The radiometer will provide extensive storm context for the radar observations, as well as observations of the convective anvils. The combination of the radars and radiometer on INCUS will deliver unprecedented three-dimensional views of tropical convective storms. INCUS is the first systematic investigation of the rapidly evolving CMF within tropical convective storms, the observations of which are expected to significantly enhance both our understanding and prediction of storm structure, their dynamics and microphysical processes, and the ways in which these evolve over storm lifetimes. This presentation will highlight the observational capabilities and scientific approach of the INCUS mission.

Susan C. van den Heever↗

NASA NTRS2004

Regional Assimilation of NASA Atmospheric Infrared Sounder (AIRS) Data

The NASA Short-term Prediction Research and Transition (SPORT) Center seeks to accelerate the infusion of NASA Earth Science Enterprise (ESE) observations, data assimilation and modeling research into NW S forecast operations and decision-making. The Atmospheric Infrared Sounder (AIRS), is expected to advance climate research and weather prediction into the 21 st century. It is one of six instruments onboard Aqua, a satellite that is part of NASA s Earth Observing System. AIRS, along with two partner microwave sounding instruments, represents the most advanced atmospheric sounding system ever deployed in space. The system is capable of measuring the atmospheric temperature in the troposphere with radiosonde accuracies of 1 K over 1 km-thick layers under both clear and cloudy conditions, while the accuracy of the derived moisture profiles will exceed that obtained by radiosondes. It is imperative that the scientific community is prepared to take full advantage of next-generation satellite data that will become available within the next decade. The purpose of this paper is to describe a procedure designed to optimally assimilate AIRS data at high spatial resolution over both land and ocean. The assimilation system used in this study is the Local Analysis and Prediction System (LAPS) developed at the Forecast System Laboratory used extensively around the globe. Results will focus on quality control issues associated with AIRS, optimal assimilation strategies, and the impact of the AIRS data on subsequent numerical forecasts at 12 km produced by the next generation Weather Research and Forecast (WRF) model.

Chou, Shih-Hung↗

DOE OSTI2019

The ARM Radar Network: At the Leading-edge of Cloud and Precipitation Observations

The ARM observatories include radars that span a range of frequencies and capabilities to characterize the three-dimensional structure and properties of atmospheric hydrometeors. Improving our ability to predict future weather and climate conditions is strongly linked to achieving significant advancements in our understanding of cloud and precipitation processes. Observations are critical to making these advancements because they both improve our understanding of these processes and provide constraints on numerical models. Historically, instruments for observing cloud properties have limited cloud-aerosol investigations to a small subset of cloud-process interactions. To address these challenges, the last decade has seen the U.S. DOE ARM facility significantly upgrade and expand its surveillance radar capabilities towards providing holistic and multi-scale observations of clouds and precipitation. These upgrades include radars that operate at four frequency bands covering a wide range of scattering regimes, improving upon the information contained in earlier ARM observations. The traditional ARM emphasis on the vertical column is maintained, providing more comprehensive, calibrated, and multiparametric measurements of clouds and precipitation. Furthermore, the ARM radar network now features multiple scanning dual-polarization Doppler radars to exploit polarimetric and multi-Doppler capabilities that provide a wealth of information on storm microphysics and dynamics under a wide range of conditions. While the diversity in wavelengths and detection capabilities are unprecedented, there is still considerable work ahead before the full potential of these radar advancements is realized. This includes synergy with other observations, improved forward and inverse modeling methods, and well-designed data-model integration methods. The overarching goal is to offer a comprehensive characterization of a complete volume of the cloudy atmosphere and to act as a natural laboratory for the study of cloud processes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

Forecast Impact Simulation Studies

A series of simulation experiments is being conducted as a cooperative effort between the European Center for Medium Range Weather Forecasts (ECMWF), the National Meteorological Center (NMC) and the Goddard Laboratory for Atmospheres (GLA), to provide a quantitative assessment of the potential impact of proposed observing systems on large scale numerical weather prediction. For these studies an advanced analysis/forecast simulation system was developed which provides for a more realistic assessment of the impact of proposed observing systems than was possible in earlier studies. This system consists of four elements: (1) An atmospheric model integration to provide a complete record of the true state of the atmosphere (called nature); (2) a conventional data assimilation cycle that is used as the control experiment; (3) a satellite data assimilation that differs from the control in also including fabricated satellite data incorporated in the forecast-analysis cycle; and (4) forecasts produced from both control and satellite initial conditions. Comparison of these forecasts with nature provides an assessment of the impact of satellite data.

Atlas, R.↗

NASA NTRS2018

Forecasting and Monitoring Intense Thunderstorms in the Hindu Kush Himalayan Region: Spring 2018 Forecasting Experiment

Some of the most intense thunderstorms on the planet occur in the Hindu Kush Himalayan (HKH) region of South Asia - where many organizations lack the capacity needed to predict, observe and/or effectively respond to the threats associated with high-impact convective weather. Among the hazards include tornadoes, damaging straight-line winds (known as Nor'westers in the HKH region), large hail, and flash flooding, which typically peak in the pre-wet-monsoon season. Previous studies have documented a disproportionately large number of casualties associated with intense thunderstorms in this region; therefore, the goal of this project is to increase situational awareness of these hazards through short-term modeling and satellite assessment tools.

remote sensing↗

NASA NTRS2013

Community-wide Validation of Geospace Model Ground Magnetic Field Perturbation Predictions to Support Model Transition to Operations

In this paper we continue the community-wide rigorous modern space weather model validation efforts carried out within GEM, CEDAR and SHINE programs. In this particular effort, in coordination among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), modelers, and science community, we focus on studying the models' capability to reproduce observed ground magnetic field fluctuations, which are closely related to geomagnetically induced current phenomenon. One of the primary motivations of the work is to support NOAA SWPC in their selection of the next numerical model that will be transitioned into operations. Six geomagnetic events and 12 geomagnetic observatories were selected for validation.While modeled and observed magnetic field time series are available for all 12 stations, the primary metrics analysis is based on six stations that were selected to represent the high-latitude and mid-latitude locations. Events-based analysis and the corresponding contingency tables were built for each event and each station. The elements in the contingency table were then used to calculate Probability of Detection (POD), Probability of False Detection (POFD) and Heidke Skill Score (HSS) for rigorous quantification of the models' performance. In this paper the summary results of the metrics analyses are reported in terms of POD, POFD and HSS. More detailed analyses can be carried out using the event by event contingency tables provided as an online appendix. An online interface built at CCMC and described in the supporting information is also available for more detailed time series analyses.

MAGNETIC↗

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

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 NTRS2023

Comparing Magnetopause Predictions From Two MHD Models During A Geomagnetic Storm and A Quiet Period

Magnetopause location is an important prediction of numerical simulations of the magnetosphere, yet the models can err, either under-predicting or over-predicting the motion of the boundary. This study compares results from two of the most widely used magnetohydrodynamic (MHD) models, the Lyon–Fedder–Mobarry (LFM) model and the Space Weather Modeling Framework (SWMF), to data from the GOES 13 and 15 satellites during the geomagnetic storm on 22 June 2015, and to THEMIS A, D, and E during a quiet period on 31 January 2013. The models not only reproduce the magnetopause crossings of the spacecraft during the storm, but they also predict spurious magnetopause motion after the crossings seen in the GOES data. We investigate the possible causes of the over-predictions during the storm and find the following. First, using different ionospheric conductance models does not significantly alter predictions of the magnetopause location. Second, coupling the Rice Convection Model (RCM) to the MHD codes improves the SWMF magnetopause predictions more than it does for the LFM predictions. Third, the SWMF produces a stronger ring current than LFM, both with and without the RCM and regardless of the LFM spatial resolution. During the non-storm event, LFM predicts the THEMIS magnetopause crossings due to the southward interplanetary magnetic field better than the SWMF. Additionally, increasing the LFM spatial grid resolution improves the THEMIS predictions, while increasing the SWMF grid resolutions does not.

magnetohydrodynamics↗

NASA NTRS2005

Modeling and Simulation for Realistic Propagation Environments of Communications Signals at SHF Band

In this article, most of widely accepted radio wave propagation models that have proven to be accurate in practice as well as numerically efficient at SHF band will be reviewed. Weather and terrain data along the signal's paths can be input in order to more accurately simulate the propagation environments under particular weather and terrain conditions. Radio signal degradation and communications impairment severity will be investigated through the realistic radio propagation channel simulator. Three types of simulation approaches in predicting signal's behaviors are classified as: deterministic, stochastic and attenuation map. The performance of the simulation can be evaluated under operating conditions for the test ranges of interest. Demonstration tests of a real-time propagation channel simulator will show the capabilities and limitations of the simulation tool and underlying models.

simulation↗

NASA NTRS2012

Evaluation of Cloud Microphysics in JMA-NHM Simulations Using Bin or Bulk Microphysical Schemes through Comparison with Cloud Radar Observations

Numerical weather prediction (NWP) simulations using the Japan Meteorological Agency NonhydrostaticModel (JMA-NHM) are conducted for three precipitation events observed by shipborne or spaceborneW-band cloud radars. Spectral bin and single-moment bulk cloud microphysics schemes are employed separatelyfor an intercomparative study. A radar product simulator that is compatible with both microphysicsschemes is developed to enable a direct comparison between simulation and observation with respect to theequivalent radar reflectivity factor Ze, Doppler velocity (DV), and path-integrated attenuation (PIA). Ingeneral, the bin model simulation shows better agreement with the observed data than the bulk modelsimulation. The correction of the terminal fall velocities of snowflakes using those of hail further improves theresult of the bin model simulation. The results indicate that there are substantial uncertainties in the masssizeand sizeterminal fall velocity relations of snowflakes or in the calculation of terminal fall velocity of snowaloft. For the bulk microphysics, the overestimation of Ze is observed as a result of a significant predominanceof snow over cloud ice due to substantial deposition growth directly to snow. The DV comparison shows thata correction for the fall velocity of hydrometeors considering a change of particle size should be introducedeven in single-moment bulk cloud microphysics.

cloud radar↗