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NASA NTRS2007

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision at the Shuttle Landing Facility. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAFs), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. Both the SMG and the MLB are currently implementing the Weather Research and Forecasting Environmental Modeling System (WRF EMS) software into their operations. The WRF EMS software allows users to employ both dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model- the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and NWS MLB with a lot of flexibility. It also creates challenges, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and to determine which configuration will best predict warm season convective initiation in East-Central Florida. Four different combinations of WRF initializations will be run (ADAS-ARW, ADAS-NMM, LAPS-ARW, and LAPS-NMM) at a 4-km resolution over the Florida peninsula and adjacent coastal waters. Five candidate convective initiation days using three different flow regimes over East-Central Florida will be examined, as well as two null cases (non-convection days). Each model run will be integrated 12 hours with three runs per day, at 0900, 1200, and 1500 UTe. ADAS analyses will be generated every 30 minutes using Level II Weather Surveillance Radar-1988 Doppler (WSR-88D) data from all Florida radars to verify the convection forecast. These analyses will be run on the same domain as the four model configurations. To quantify model performance, model output will be subjectively compared to the ADAS analyses of convection to determine forecast accuracy. In addition, a subjective comparison of the performance of the ARW using a high-resolution local grid with 2-way nesting, I-way nesting, and no nesting will be made for select convective initiation cases. The inner grid will cover the East-Central Florida region at a resolution of 1.33 km. The authors will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for predicting warm season convective initiation over East-Central Florida.

Watson, Leela R.↗
NASA NTRS2017

Surface Ozone in the Colorado Northern Front Range and the Influence of Oil and Gas Development During FRAPPE/DISCOVER-AQ in Summer 2014

High mixing ratios of ozone (O3) in the northern Front Range (NFR) of Colorado are not limited to the urban Denver area but were also observed in rural areas where oil and gas activity is the primary source of O3 precursors. On individual days, oil and gas O3 precursors can contribute in excess of 30 ppb to O3 growth and can lead to exceedances of the EPA O3 National Ambient Air Quality Standard. Data used in this study were gathered from continuous surface O3 monitors for June-August 2013-2015 as well as additional flask measurements and mobile laboratories that were part of the FRAPPE/DISCOVER-AQ field campaign of July-August 2014. Overall observed O3 levels during the summer of 2014 were lower than in 2013, likely due to cooler and damper weather than an average summer. This study determined the median hourly surface O3 mixing ratio in the NFR on summer days with limited photochemical production to be approximately 45-55 ppb. Mobile laboratory and flask data collected on three days provide representative case studies of different O3 formation environments in and around Greeley, Colorado. Observations of several gases (including methane, ethane, CO, nitrous oxide) along with O3 are used to identify sources of O3 precursor emissions. A July 23 survey demonstrated low O3 (45-60 ppb) while August 3 and August 13 surveys recorded O3 levels of 75-80 ppb or more. August 3 exemplifies influence of moderate urban and high oil and gas O3 precursor emissions. August 13 demonstrates high oil and gas emissions, low agricultural emissions, and CO measurements that were well correlated with ethane from oil and gas, suggesting an oil and gas related activity as a NOx and O3 precursor source. Low isoprene levels indicated that they were not a significant contributor to O3 precursors measured during the case studies.

Pollution↗
NASA NTRS2007

Uncertainty Analysis of Historical Hurricane Data

An analysis of variance (ANOVA) study was conducted for historical hurricane data dating back to 1851 that was obtained from the U. S. Department of Commerce National Oceanic and Atmospheric Administration (NOAA). The data set was chosen because it is a large, publicly available collection of information, exhibiting great variability which has made the forecasting of future states, from current and previous states, difficult. The availability of substantial, high-fidelity validation data, however, made for an excellent uncertainty assessment study. Several factors (independent variables) were identified from the data set, which could potentially influence the track and intensity of the storms. The values of these factors, along with the values of responses of interest (dependent variables) were extracted from the data base, and provided to a commercial software package for processing via the ANOVA technique. The primary goal of the study was to document the ANOVA modeling uncertainty and predictive errors in making predictions about hurricane location and intensity 24 to 120 hours beyond known conditions, as reported by the data set. A secondary goal was to expose the ANOVA technique to a broader community within NASA. The independent factors considered to have an influence on the hurricane track included the current and starting longitudes and latitudes (measured in degrees), and current and starting maximum sustained wind speeds (measured in knots), and the storm starting date, its current duration from its first appearance, and the current year fraction of each reading, all measured in years. The year fraction and starting date were included in order to attempt to account for long duration cyclic behaviors, such as seasonal weather patterns, and years in which the sea or atmosphere were unusually warm or cold. The effect of short duration weather patterns and ocean conditions could not be examined with the current data set. The responses analyzed were the storm latitude, longitude and intensity, as recorded in the data set, 24 or 120 hours beyond the current state. Several ANOVA modeling schemes were examined. Two forms of validation were used: 1) comparison with official hurricane prediction performance metrics and 2) cases studies conducted on hurricanes from the 2005 season, which were not included within the model construction and ANOVA assessment. In general, the ANOVA technique did not perform as well as the established official prediction performance metrics published by NOAA; still, the technique did remarkably well in this demonstration with a difficult data set and could probably be made to perform better with more knowledge of hurricane development and dynamics applied to the problem. The technique provides a repeatable prediction process that eliminates the need for judgment in the forecast.

Green, Lawrence L.↗
NASA NTRS1976

Test facility for solar-cell reference conditions

A test facility, intended primarily for long-term monitoring of the global insolation and its components and the concurrent solar cell performance under a wide variety of measureable atmospheric and weather conditions, is described. Instruments for the measurement of insolation, cell performance, turbidity, water vapor, and cloud cover are described. Preliminary evaluation of the hourly data base generated over a two-month period for a range of sky conditions from clear to overcast is presented.

Klucher, T. M.↗
NASA NTRS1980

Climatological relationships of severe duststorms in the Great Plains to synoptic weather patterns: A potential for predictability

A data base provided by 35 severe duststorms that occurred between 1968 and 1977 in the central and southern Great Plains allowed construction of a classification scheme of meteorological causes of duststorms, and a telescopic forecast technique for medium range (6 to 48 hour) prediction of severe cyclogenic duststorms. In addition, areal coverage definitions for duststorms based on characteristics of the storms, and a hierarchy of weather causes of severe duststorms were developed. The man machine mix forecast correctly predicted six of seven duststorms observed during the 1976-77 winter, with one overforecast; the machine-only forecast correctly predicted four of the seven duststorms, with one overforecast. Both techniques had problems correctly predicting the duration of severe duststorms.

Woiceshyn, P. M.↗
NASA NTRS1983

Short-range quantitative precipitation forecasts using a divergent initialization

Data simulations and real data tests indicate that the treatment of the initial divergence field in a mesoscale weather prediction model strongly influences the quality of the first 6 hours of a rainfall forecast. Of the procedures tested for determining the initial divergence field for the predictions, the best technique is found to be the Tarbell et al. (1981) diagnostic procedure, where a diabatic form of the omega equation is used to obtain a three-dimensional vertical motion field that in turn can be used in conjunction with a continuity equation to obtain the horizontal divergence fields.

Salmon, E. M.↗
NASA NTRS1986

Initial results of the Global Thermospheric Mapping Study (GTMS)

The Global Thermospheric Mapping Study (GTMS) is a multi-technique experimental study of the thermosphere designed to map simultaneously its spatial and temporal morphology with a thoroughness and diversity of measurement techniques heretofore unachieved. The GTMS is designed around the Incoherent Scatter Radar Chain in the western hemisphere. The European incoherent scatter radars and the worldwide communities of Fabry-Perot interferometers, meteor wind radars, partial reflection drifts radars, MST radars, and satellite probes are included to extend the spatial coverage and types of measurements available. Theoretical and modeling support in the areas of thermospheric and ionospheric structure, tides, and electric fields are included to aid in program planning and data interpretation. Solar activity was low on the three observation days (F10.7 = 97, 98, 96) and magnetic conditions were unsettled to active (A = 10, 12, 20). All six incoherent scatter radar facilities collected data. Each collected F region data day and night while Saint Santin and Millstone Hill additionally collected E region data during daylight hours. Initial results from Sondrestrom and Millstone Hill are presented. Good quality Fabry Perot data were collected at Fritz Peak and San Jose dos Campos. Weather conditions produced poor results at Arequipa and Arecibo. Initial results from Fritz Peak are presented. Mesosphere/lower-thermosphere observations were conducted under the ATMAP organization. The magnetometer chains also were operational during this campaign. Initial thermospheric general circulation model predictions were made for assumed solar-geophysical conditions, and selected results are presented.

Oliver, W. L.↗
NASA NTRS2002

Evaluation of Refractivity Profiles from CHAMP and SAC-C GPS Radio Occultation

The CHAMP and SAC-C missions are the first missions to carry a second-generation 'Blackjack' GPS receiver. One of the new features of this receiver is its ability to sense the lower troposphere closer to the surface than the proof-of-concept GPS/MET 1995 experiment. Since their launch, CHAMP and SAC-C have collected thousands of GPS radio occultations, representing a wealth of measurements available for data assimilation in Numerical Weather Prediction (NWP) models. In order to evaluate the refractivity data derived by JPL from raw radio occultation measurements, we use Data Assimilation Office (DAO) 6-hour forecasts as an independent state of the atmosphere. We compare CHAMP and SAC-C refractivity (processed by JPL) with refractivity calculated from the DAO global fields of temperature, water vapor content and humidity. We will show statistics of the differences as well as Probability Density Functions (PDFs) of the differences. Depending upon availability of AIRS data, we plan to show individual profile comparisons between GPS radio occultation and AIRS retrievals.

Poli, Paul↗
NASA NTRS2008

Surface Landing Site Weather Analysis for Constellation Program

Weather information is an important asset for NASA's Constellation Program in developing the next generation space transportation system to fly to the International Space Station, the Moon and, eventually, to Mars. Weather conditions can affect vehicle safety and performance during multiple mission phases ranging from pre-launch ground processing to landing and recovery operations, including all potential abort scenarios. Meteorological analysis is an important contributor, not only to the development and verification of system design requirements but also to mission planning and active ground operations. Of particular interest are the surface atmospheric conditions at both nominal and abort landing sites for the manned Orion capsule. Weather parameters such as wind, rain, and fog all play critical roles in the safe landing of the vehicle and subsequent crew and vehicle recovery. The Marshall Space Flight Center Natural Environments Branch has been tasked by the Constellation Program with defining the natural environments at potential landing zones. Climatological time series of operational surface weather observations are used to calculate probabilities of occurrence of various sets of hypothetical vehicle constraint thresholds, Data are available for numerous geographical locations such that statistical analysis can be performed for single sites as well as multiple-site network configurations. Results provide statistical descriptions of how often certain weather conditions are observed at the site(s) and the percentage that specified criteria thresholds are matched or exceeded. Outputs are tabulated by month and hour of day to show both seasonal and diurnal variation. This paper will describe the methodology used for data collection and quality control, detail the types of analyses performed, and provide a sample of the results that can be obtained,

Altino, Karen M.↗
NASA NTRSDate not supplied

Mapping Surface Vapor Pressure Deficits From Geostationary Satellites for Fire Weather Monitoring

The increase in the wildfires were observed globally in accordance with global warming, and to real- time monitoring of wildfire risk in broad scale is demanded for wildfire management to prevent the spread of wildfires. Scientists invented a lot of indices to assess the wildfire risk. Vapor Pressure Deficit (VPD) is one of the most important meteorological components for those indices. Compared to other components of fire weather indices, VPD can change quickly from lower risk to higher risk even in sub-hourly. Therefore, real-time fire risk monitoring requires high-resolution and high- temporal VPD spatial map. Here, we developed VPD estimation method using the GOES Advanced Baseline Imager (ABI) data. Unlike the polar-orbital satellite data, the ABI can observe target region every 10 minutes, so that we can estimate VPD for fire weather in real-time. The method used to estimate VPD is same with the algorithm of NASA Earth Exchange Gridded Daily Meteorology (NEX- GDM), which estimate meteorological variables from ground weather observation and spatial variables based on random forest (RF). We calculated RF importance to select bands of ABI as input of the model. To validate our results, we compared the spatial pattern of our VPD data with the Real- Time Mesoscale Analysis (RTMA) data over the conterminous USA. We sought possibility of applying our method to the region where no real-time high-resolution weather data is available, such as South America. The developed method can produce real-time high-resolution high-frequent VPD data in the continental scale. The derived data from GOES ABI could contribute to improve the fire weather monitoring and lead to prevent wildfires.

Hirofumi Hashimoto↗
NASA NTRS1988

Bracknell Meteorological Office

The Bracknell (U.K.) Meteorological Office runs a global weather model twice a day, providing the following data: surface and radiosonde; aircraft reports; and satellite soundings and wind. A human forecast is made every six hours. The model runs on a 150 km grid with 15 levels, and takes about four minutes on a Cyber-205. The standard output from the global products are wind, temperature, height, tropopause, and maximum wind. Various experiments have been conducted to see if short-range forecasters could improve on the upper-wind forecasts over the numerical model; the numerical model remains of paramount importance. Small-scale models are being run in the U.S. and the U.K. A fine-mesh model covers Europe and the Atlantic. A mesoscale model is under development. A great deal of verification work is done to see how good the models are.

Flood, Colin R.↗
NASA NTRS1973

Monitor weather conditions for cloud seeding control

The author has identified the following significant results. The near real-time DCS platform data transfer to the time-share compare is a working reality. Six stations are now being automatically monitored and displayed with a system delay of 3 to 8 hours from time of data transmission to time of data accessibility on the computer. The DCS platform system has proven itself a valuable tool for near real-time monitoring of mountain precipitation. Data from Wolf Creek Pass were an important input in making the decision when to suspend seeding operations to avoid exceeding suspension criteria in that area. The DCS platforms, as deployed in this investigation, have proven themselves to be reliable weather resistant systems for winter mountain environments in the southern Colorado mountains.

Kahan, A. M.↗
NASA NTRS2019

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Developments in ocean data assimilation (DA) and observing system technologies are intertwined. New observation types lead to new DA methods, and new DA methods such as Coupled Data Assimilation can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners are encouraged to make better use of observations that are already available, for example in strongly coupled data assimilation where ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate,as well as initializing operational long-range prediction models. There are remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean observing system throughout its history, the presence of biases and drifts in models, and simplifying assumptions made in the DA methods. From a governance point of view, more support is needed to interface the observing community and the ocean DA community. For prediction applications, the ocean DA community must work with the ocean observing community to establish protocols for rapid communication of ocean observing data on NWP timescales. There is potential for new observations to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of numerical weather prediction covering hours to weeks, out to multiple decades. It is highly encouraged that communication be fostered between thesecommunities to allow operational prediction centers the ability to provide guidance to the design of a sustained and adaptive observing network.

Ocean reanalysis↗
NASA NTRSDate not supplied

Novel Hourly-Resolved Global Cloud Property Composite from Operational Satellites Imagers

Numerous applications in satellite remote sensing of surface properties, atmospheric state, composition, and radiation require accurate knowledge of the location and characteristics of clouds. Operational satellite imager radiances are valuable for cloud detection and for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. This paper describes a new global high-resolution dataset of cloud properties made available for community use that is constructed from analyses of a constellation of meteorological satellite imagers. Data taken from Meteosat-8/9 and -11, Himawari-8/9, GOES-16, and -17/-18, Aqua, Terra, Suomi-NPP and NOAA-20 are processed and composited on a 3-km grid to provide hourly global coverage. A historical multi-year dataset is available to serve various needs including modeling challenges related to cloud evaluations and parameterizations. Near real-time data products are also currently available between 60N and 60S. Efforts are underway to operationalize polar orbiting satellite cloud detection methods for low-latency applications over polar regions. The cloud detection and retrieval algorithms have been developed over many years to support NASA weather and climate programs such as the Clouds and Earth’s Radiant Energy System (CERES). To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. A brief description of the methods highlighting the unique aspects of the SatCORPS data products will be presented along with information on their status and availability.

SatCORPS↗
NASA NTRS2016

A New Tropical Cyclone Dynamic Initialization Technique Using High Temporal and Spatial Density Atmospheric Motion Vectors and Airborne Field Campaign Data

Background: Initialization of tropical cyclones in numerical weather prediction (NWP) systems is a great challenge: Mass-wind field balance; Secondary circulation and heating; Asymmetries. There can be large adjustments in structure and intensity in the first 24 hours if the initial vortex is not in balance: Spurious gravity waves; Spin-up (model and physics). Existing mesoscale NWP model TC (Tropical Cyclone) initialization strategies: Bogus vortex, cold start from global analyses; 3DVAR or 4DVAR, possibly with synthetic observations; EnKF (Ensemble Kalman Filter); Dynamic initialization. Dynamic initialization allows vortex to have improved balance and physics spin-up at the initial time (e.g., Hendricks et al. 2013, 2011; Nguyen and Chen 2011; Fiorino and Warner 1981; Hoke and Anthes 1976). Himawari-8 geostationary satellite has capability of continuous imagery (10-minutes) over the full disk: New GOES-R satellites will have same capability. This will allow for unprecedented observations of tropical cyclones. However, current data assimila1on systems are not capable of ingesting such high temporal observations (Atmospheric Mo1on Vectors - AMVs). Hourly AMVs are produced, and thinned to 100-kilometer spacing in the horizontal. An entirely new data assimilation concept is required to utilize these observations.

remote sensing↗
NASA NTRS2003

The NASA Short-term Prediction Research and Transition (SPoRT) Center: A Collaborative Model for Accelerating Research into Operations

The NASA Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, Alabama was created to accelerate the infusion of NASA earth science observations, data assimilation and modeling research into NWS forecast operations and decision-making. The principal focus of experimental products is on the regional scale with an emphasis on forecast improvements on a time scale of 0-24 hours. The SPoRT Center research is aligned with the regional prediction objectives of the US Weather Research Program dealing with 0-1 day forecast issues ranging from convective initiation to 24-hr quantitative precipitation forecasting. The SPoRT Center, together with its other interagency partners, universities, and the NASA/NOAA Joint Center for Satellite Data Assimilation, provides a means and a process to effectively transition NASA Earth Science Enterprise observations and technology to National Weather Service operations and decision makers at both the global/national and regional scales. This paper describes the process for the transition of experimental products into forecast operations, current products undergoing assessment by forecasters, and plans for the future.

Goodman, S. J.↗
NASA NTRS1994

Lightning studies using LDAR and companion data sets

Research was conducted to use the KSC Lightning Detection and Ranging (LDAR) system, together with companion data, in four subprojects: weather forecasting and advisory applications of LDAR, LDAR in relation to field mill readings, lightning flash and stroke detection using LDAR, and LDAR in relation to radar reflectivity patterns and KSC wind profiler vertical velocities. The research is aimed at developing rules, algorithms, and training materials that can be used by the operational weather forecasters who issue weather advisories for daily ground operations and launches by NASA and the United States Air Force. During the summer of 1993, LDAR data was examined on an hourly basis from 14 thunderstorm days and compared to ground strike data measured by the Lightning Location and Protection (LLP) system. These data were re-examined during 1994 to identify, number, and track LDAR-detected storms continually throughout the day and avoid certain interpretation problems arising from the use of hourly files. An areal storm growth factor was incorporated into a scheme to use current mappings of LDAR-defined thunderstorms to predict future ground strikes. During the summer of 1994, extensive sets of LDAR and companion data have been collected for 16 thunderstorm days, including a variety of meteorological situations. Detailed case studies are being conducted to relate the occurence of LDAR to the radar structure and evolution of thunderstorms. Field mill (LPWS) data are being examined to evaluate the complementary nature of LDAR and LPLWS data in determining the time of beginning and ending of the ground strike threat at critical sites. A computerized lightning flash and stroke discrimination algorithm has been written that can be used to help locate the points of origin of the electrical discharges, help distinguish in-cloud, cloud-ground, and upward flashes, and perhaps determine when the threat of ground strikes has ceased. Surface wind tower (mesonet), radar, sounding, and KSC wind profiler data will be used to develop schemes to help anticipate the timing and location of new thunderstorm development. Analysis of this data will continue in graduate student research projects.

Forbes, Gregory S.↗
NASA NTRS2012

Applications of NASA and NOAA Satellite Observations by NASA's Short-term Prediction Research and Transition (SPoRT) Center in Response to Natural Disasters

NASA s Short-term Prediction Research and Transition (SPoRT) Center supports the transition of unique NASA and NOAA research activities to the operational weather forecasting community. SPoRT emphasizes real-time analysis and prediction out to 48 hours. SPoRT partners with NOAA s National Weather Service (NWS) Weather Forecast Offices (WFOs) and National Centers to improve current products, demonstrate future satellite capabilities and explore new data assimilation techniques. Recently, the SPoRT Center has been involved in several activities related to disaster response, in collaboration with NOAA s National Weather Service, NASA s Applied Sciences Disasters Program, and other partners.

Molthan, Andrew L.↗