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NASA NTRS2014

Meningitis and Climate: From Science to Practice

Meningococcal meningitis is a climate sensitive infectious disease. The regional extent of the Meningitis Belt in Africa, where the majority of epidemics occur, was originally defined by Lapeysonnie in the 1960s. A combination of climatic and environmental conditions and biological and social factors have been associated to the spatial and temporal patterns of epidemics observed since the disease first emerged in West Africa over a century ago. However, there is still a lack of knowledge and data that would allow disentangling the relative effects of the diverse risk factors upon epidemics. The Meningitis Environmental Risk Information Technologies Initiative (MERIT), a collaborative research-to-practice consortium, seeks to inform national and regional prevention and control strategies across the African Meningitis Belt through the provision of new data and tools that better determine risk factors. In particular MERIT seeks to consolidate a body of knowledge that provides evidence of the contribution of climatic and environmental factors to seasonal and year-to-year variations in meningococcal meningitis incidence at both district and national scales. Here we review recent research and practice seeking to provide useful information for the epidemic response strategy of National Ministries of Health in the Meningitis Belt of Africa. In particular the research and derived tools described in this paper have focused at "getting science into policy and practice" by engaging with practitioner communities under the umbrella of MERIT to ensure the relevance of their work to operational decision-making. We limit our focus to that of reactive vaccination for meningococcal meningitis. Important but external to our discussion is the development and implementation of the new conjugate vaccine, which specifically targets meningococcus A

Mineral dust↗
NASA NTRS2019

Global Assimilation of L-Band Brightness Temperature Observations from SMAP and SMOS into the Catchment Land Surface Model and Contribution to the Skill of Soil Moisture Estimates

The Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity (SMOS) missions provide global observations of L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations at a resolution of ~40 km every 2-3 days. These observations have been available since 2015 from SMAP and since 2010 from SMOS.Soil moisture estimates from the separate assimilation of SMAP and SMOS Tb observations into land surface models were previously shown to improve over model-only estimates, thereby demonstrating the value of assimilating L-band Tb observations for soil moisture estimation. The assimilation experiments documented in the literature do not, however, establish whether the joint assimilation of SMAP and SMOS Tbs improves the skill of the resulting soil moisture estimates beyond what can be achieved with either set of observations alone. Moreover, because the published SMAP and SMOS results used different assimilation systems and configurations and applied different evaluation data and procedures, it is unclear whether the assimilation of SMAP-only or SMOS-only Tbs results in better soil moisture skill. In this presentation, we compare the results of three separate assimilation experiments for the period from April 2015 to present. The experiments utilize the SMAP Level-4 Soil Moisture (L4_SM) algorithm, which assimilates L-band Tb observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. Specifically, the three experiments presented here assimilate (i) SMAP Tbs only, as in the L4_SM product, (ii) SMOS Tbs only, after interpolation of 40° incidence angle, and (iii) both SMAP and SMOS Tbs. In all other respects, the configuration of three experiments is identical, as is the validation of the output. Preliminary results suggest that the joint assimilation of SMAP and SMOS Tbs yields the most skillful soil moisture estimates when compared to independent in situ measurements. Moreover, the skill of the SMAP-only assimilation exceeds that of the SMOS-only assimilation. The presentation provides an in-depth evaluation of the skill of the assimilation estimates vs. independent in situ and satellite measurements and in terms of statistics of the observation-minus-forecast residuals and analysis increments.

Reichle, Rolf↗
NASA NTRSDate not supplied

The OSSE Framework at the NASA Global Modeling and Assimilation Office (GMAO)

The Global Modeling and Assimilation Office (GMAO) has been developing an Observing System Simulation Experiment (OSSE) framework. The OSSE system is currently based on the Nature Run (NR) developed by GMAO called G5NR. The G5NR is currently being tested for use in the GMAO OSSE framework. Synthetic observations have been generated based on the G5NR fields, including conventional observations, GPS, and satellite radiances. These synthetic observations are ingested using the Gridpoint Statistical Interpolation data assimilation system, with forecasts performed by the GEOS-5 model at 55 km/72L.

OSSE Framework↗
NASA NTRS2006

Global Positioning System (GPS) Precipitable Water in Forecasting Lightning at Spaceport Canaveral

This paper evaluates the use of precipitable water (PW) from Global Positioning System (GPS) in lightning prediction. Additional independent verification of an earlier model is performed. This earlier model used binary logistic regression with the following four predictor variables optimally selected from a candidate list of 23 candidate predictors: the current precipitable water value for a given time of the day, the change in GPS-PW over the past 9 hours, the KIndex, and the electric field mill value. This earlier model was not optimized for any specific forecast interval, but showed promise for 6 hour and 1.5 hour forecasts. Two new models were developed and verified. These new models were optimized for two operationally significant forecast intervals. The first model was optimized for the 0.5 hour lightning advisories issued by the 45th Weather Squadron. An additional 1.5 hours was allowed for sensor dwell, communication, calculation, analysis, and advisory decision by the forecaster. Therefore the 0.5 hour advisory model became a 2 hour forecast model for lightning within the 45th Weather Squadron advisory areas. The second model was optimized for major ground processing operations supported by the 45th Weather Squadron, which can require lightning forecasts with a lead-time of up to 7.5 hours. Using the same 1.5 lag as in the other new model, this became a 9 hour forecast model for lightning within 37 km (20 NM)) of the 45th Weather Squadron advisory areas. The two new models were built using binary logistic regression from a list of 26 candidate predictor variables: the current GPS-PW value, the change of GPS-PW over 0.5 hour increments from 0.5 to 12 hours, and the K-index. The new 2 hour model found the following for predictors to be statistically significant, listed in decreasing order of contribution to the forecast: the 0.5 hour change in GPS-PW, the 7.5 hour change in GPS-PW, the current GPS-PW value, and the KIndex. The new 9 hour forecast model found the following five independent variables to be statistically significant, listed in decreasing order of contribution to the forecast: the current GPSPW value, the 8.5 hour change in GPS-PW, the 3.5 hour change in GPS-PW, the 12 hour change in GPS-PW, and the K-Index. In both models, the GPS-PW parameters had better correlation to the lightning forecast than the K-Index, a widely used thunderstorm index. Possible future improvements to this study are discussed.

Kehrer, Kristen C.↗
NASA NTRS2006

Analysis of Rapidly Developing Low Cloud Ceilings in a Stable Environment

Forecasters at the Space Meteorology Group (SMG) issue 30 to 90 minute forecasts for low cloud ceilings at the Space Shuttle Landing Facility (TTS) to support Space Shuttle landings. Mission verification statistics have shown ceilings to be the number one forecast challenge for SMG. More specifically, forecasters at SMG are concerned with any rapidly developing clouds/ceilings below 8000 ft in a stable, capped thermodynamic environment. Therefore, the Applied Meteorology Unit (AMU) was tasked to examine archived events of rapid stable cloud formation resulting in ceilings below 8000 ft, and document the atmospheric regimes favoring this type of cloud development. The AMU examined the cool season months of November to March during the years of 1993-2003 for days that had low-level inversions and rapid, stable low cloud formation that resulted in ceilings violating the Space Shuttle Flight Rules. The AMU wrote and modified existing code to identify inversions from the morning (-10 UTC) Cape Canaveral, FL rawinsonde (XMR) during the cool season and output pertinent sounding information. They parsed all days with cloud ceilings below 8000 ft at TTS, forming a database of possible rapidly-developing low ceiling events. Days with precipitation or noticeable fog burn-off situations were excluded from the database. In the first phase of this work, only the daytime hours were examined for possible ceiling development events since low clouds are easier to diagnose with visible satellite imagery. Phase II of this work includes expanding the database to include nighttime cases which is underway as this abstract is being written. For the nighttime cases, the AMU will analyze both the 00 UTC soundings and the 10 UTC soundings to examine those data for the presence of a low-level inversion. The 00 UTC soundings will probably not have a surface-based inversion, but the presence of inversions or "neutral" layers aloft and below 8,000 ft will most likely help define the stable regime, being a thermodynamically "capped" environment. Occurrences of elevated low-level inversions or stable layers will be highlighted in conjunction with nights that experienced a possible development or onset of cloud ceilings below 8,000 ft. Using these criteria to narrow down the database, the AMU will then use archived IR satellite imagery for these possible events. This presentation summarizes the composite meteorological conditions for 20 daytime event days with rapid low cloud ceiling formation and 48 non-events days consisting of advection or widespread low cloud ceilings and describes two sample cases of daytime rapidly-developing low cloud ceilings. The authors will also summarize the work from the nighttime cases and describe a representative sample case from this data set.

Bauman, William H., III↗
NASA NTRS1999

Real-Time Assimilation of Goes-Derived Products into A Mesoscale Model and It's Impact on Short-Term (06-36hr) Forecasts from 17 October 1998 through the Present

As the parameterizations of surface energy budgets in regional models have become more complete physically, models have the potential to be much more realistic in simulations of coupling between surface radiation, hydrology, and surface energy transfer. Realizing the importance of properly specifying the surface energy budget, many institutions are using land-surface models to represent the lower boundary forcing associated with biophysical processes and soil hydrology. However, the added degrees of freedom due to inclusion of such land-surface schemes require the specification of additional parameters within the model system such as vegetative resistances, green vegetation fraction, leaf area index, soil physical and hydraulic characteristics, stream flow, runoff, and the vertical distribution of soil moisture. Spatial heterogeneity of these parameters makes correct specification problematic since measurements are not routinely available. A technique has been developed for assimilating GOES-IR skin temperature tendencies, solar insolation, and surface albedo into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. The technique has been successfully employed in a number of mesoscale models in case-study mode. We have taken the next step and developed a study to determine if assimilating these types of data into mesoscale models in real-time can improve short-term (648h) forecasts of temperature, relative humidity, and QPF on a daily basis over relatively large regions. Therefore, an operational modeling/assimilation system has been developed at the GHCC during the past summer that allows us to produce simulations out to 48 hours in a timely manor. The PSU/NCAR MM5 is used in a nested configuration with a 25 km grid covering the southeastern third of the US. The model has been on-line since 1 July 1998 and forecast products are posted on our web site. The satellite algorithms that generate data to be assimilated came on-line 17 October 1998. Quantitative assessment of the forecast quality is performed via traditional verification statistics. In addition, invaluable qualitative information is obtained through close collaboration with several NWSFO's who are using the MM5 products in real-time on a daily basis. The assimilation technique has been applied in an off-line mode since 17 October. Results based on bulk statistical verification of surface meteorology over the entire Southeastern US show that assimilating the GOES-derived land surface tendencies and solar radiation results in a significant reduction of the shelter air temperature and RH bias on a daily basis. In fact, the assimilation technique has produced improved temperature and RH forecasts for 97% of the 100 simulations performed to date. Work is currently underway to determine the sensitivity of the assimilation procedure to the availability of satellite data, length of assimilation period, model initialization, and synoptic-scale meteorological conditions. In addition, results from a detailed energy budget analysis using the Early Eta, our operational MM5, and the assimilation runs will help us to better understand the satellite assimilation the land-surface energy budge. Research during the spring-summer of 1999 will focus on the impact of the assimilation technique during the warm season where it is hypothesized that it can have a positive impact on QPF during conditions of weak synoptic-scale forcing.

Lapenta, William M.↗
NASA NTRS1998

Evaluation of the 29-km Eta Model: Objective Verification at Three Selected Stations - Part 1

This paper describes an objective verification of the National Centers for Environmental Prediction (NCEP) 29-km eta model from May 1996 through January 1998. The evaluation was designed to assess the model's surface and upper-air point forecast accuracy at three selected locations during separate warm (May - August) and cool (October - January) season periods. In order to enhance sample sizes available for statistical calculations, the objective verification includes two consecutive warm and cool season periods. Systematic model deficiencies comprise the larger portion of the total error in most of the surface forecast variables that were evaluated. The error characteristics for both surface and upper-air forecasts vary widely by parameter, season, and station location. At upper levels, a few characteristic biases are identified. Overall however, the upper-level errors are more nonsystematic in nature and could be explained partly by observational measurement uncertainty. With a few exceptions, the upper-air results also indicate that 24-h model error growth is not statistically significant. In February and August 1997, NCEP implemented upgrades to the eta model's physical parameterizations that were designed to change some of the model's error characteristics near the surface. The results shown in this paper indicate that these upgrades led to identifiable and statistically significant changes in forecast accuracy for selected surface parameters. While some of the changes were expected, others were not consistent with the intent of the model updates and further emphasize the need for ongoing sensitivity studies and localized statistical verification efforts. Objective verification of point forecasts is a stringent measure of model performance, but when used alone, is not enough to quantify the overall value that model guidance may add to the forecast process. Therefore, results from a subjective verification of the meso-eta model over the Florida peninsula are discussed in the companion paper by Manobianco and Nutter. Overall verification results presented here and in part two should establish a reasonable benchmark from which model users and developers may pursue the ongoing eta model verification strategies in the future.

Nutter, Paul A.↗
NASA NTRS2022

Assimilation of GPM-retrieved Ocean Surface Meteorology Data for Two Snowstorm Events during ICE-POP 2018

As a component of the National Aeronautics and Space Administration (NASA) Weather Focus Area and Global Precipitation Measurement (GPM) Ground Validation participation in the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field research and forecast demonstration programs, hourly ocean surface meteorology properties were retrieved from the GPM microwave observations for January – March 2018. In this study, the retrieved ocean surface meteorological products – 2-m temperature, 2-m specific humidity, and 10-m wind speed were assimilated into a regional numerical weather prediction (NWP) framework to explore the application of these observations for two heavy snowfall events during the ICE-POP 2018: 27-28 February, and 7-8 March 2018. The Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) were used to conduct high resolution simulations and data assimilation experiments. The results indicate that the data assimilation has a large influence on surface thermodynamic and wind fields in the model initial condition for both events. With cycled data assimilation, significantly positive influence of the retrieved surface observation was found for the March case with improved quantitative precipitation forecast and reduced error in temperature forecast. A slightly smaller yet positive impact was also found in the forecast of the February case.

assimilation↗
NASA NTRS2006

Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.

Chatterji, Gano B.↗
NASA NTRS2023

Can Remotely Sensed Snow Disappearance Explain Seasonal Water Supply?

Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived Day of Snow Disappearance (DSD)—the date at which snow has completely disappeared—and the seasonal water supply, i.e., the April—July total streamflow volume, for 15 snow dominated basins across the western U.S. A Monte Carlo framework was applied, using linear regression models to evaluate the predictive skill—defined here as a model’s ability to accurately predict seasonal flow volumes—of varied predictors, including DSD and in situ snow water equivalent (SWE), across a range of spring forecast dates. In all basins there is a statistically significant relationship between mean DSD and seasonal water supply (p ≤ 0.05), with mean DSD explaining roughly half of the variance. Satellite-based model skill improves later in the forecast season, surpassing the skill of in-situ-based (SWE) models in skill in 10 of the 15 basins by the latest forecast date. We found little to no correlation between model error and basin characteristics such as elevation and the ratio of snow water equivalent to total precipitation. Despite a relatively short data record, this exploratory analysis shows promise for improving seasonal water supply prediction, in particular for snow dominated basins lacking in situ observations.

snow remote sensing↗
NASA NTRS2016

Incorporating Yearly Derived Winter Wheat Maps Into Winter Wheat Yield Forecasting Model

Wheat is one of the most important cereal crops in the world. Timely and accurate forecast of wheat yield and production at global scale is vital in implementing food security policy. Becker-Reshef et al. (2010) developed a generalized empirical model for forecasting winter wheat production using remote sensing data and official statistics. This model was implemented using static wheat maps. In this paper, we analyze the impact of incorporating yearly wheat masks into the forecasting model. We propose a new approach of producing in season winter wheat maps exploiting satellite data and official statistics on crop area only. Validation on independent data showed that the proposed approach reached 6% to 23% of omission error and 10% to 16% of commission error when mapping winter wheat 2-3 months before harvest. In general, we found a limited impact of using yearly winter wheat masks over a static mask for the study regions.

MODIS↗
NASA NTRS2006

A Study of Rapidly Developing Low Cloud Ceilings in a Stable Atmosphere at the Florida Spaceport

Forecasters at the Space Meteorology Group (SMG) issue 30 to 90 minute forecasts for low cloud ceilings at the Shuttle Landing Facility (KTTS) in Kennedy Space Center, FL for all Space Shuttle missions. Mission verification statistics have shown cloud ceilings to be the biggest forecast challenge. SMG forecasters are especially concerned with rapidly developing cloud ceilings below 8000 ft. in a stable, capped thermodynamic environment because ceilings below 8000 ft restrict Shuttle landing operations and are the most challenging to predict accurately. This project involves the development of a database of these cases over east-central Florida in order to identify the onset, location, and if possible, dissipation times of rapidly-developing low cloud ceilings. Another goal is to document the atmospheric regimes favoring this type of cloud development to improve forecast skill of such events during Space Shuttle launch and landing operations. A 10-year database of stable, rapid low cloud development days during the daylight hours was compiled for the Florida cool-season months by examining the Cape Canaveral Air Force Station sounding data, and identifying days that had high boundary layer relative humidity associated with a thermally-capped environment below 8000 ft. Archived hourly surface observations from KTTS and Melbourne, Orlando, Sanford, and Ocala, FL were then examined for the onset of cloud ceilings below 8000 ft between 1100 and 2000 UTC. Once the database was supplemented with the hourly surface cloud observations, visible satellite imagery was examined in 30-minute intervals to confirm event occurrences. This paper will present results from some of the rapidly developing cloud ceiling cases and the prevailing meteorological conditions associated with these events, focusing on potential pre-curser information that may help improve their prediction.

FROM↗
NASA NTRS2013

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Roberts, J. Brent↗
NASA NTRS2003

Variational Continuous Assimilation of TMI and SSM/I Rain Rates: Impact on GEOS-3 Hurricane Analyses and Forecasts

We describe a variational continuous assimilation (VCA) algorithm for assimilating tropical rainfall data using moisture and temperature tendency corrections as the control variable to offset model deficiencies. For rainfall assimilation, model errors are of special concern since model-predicted precipitation is based on parameterized moist physics, which can have substantial systematic errors. This study examines whether a VCA scheme using the forecast model as a weak constraint offers an effective pathway to precipitation assimilation. The particular scheme we exarnine employs a '1+1' dimension precipitation observation operator based on a 6-h integration of a column model of moist physics from the Goddard Earth Observing System (GEOS) global data assimilation system DAS). In earlier studies, we tested a simplified version of this scheme and obtained improved monthly-mean analyses and better short-range forecast skills. This paper describes the full implementation ofthe 1+1D VCA scheme using background and observation error statistics, and examines how it may improve GEOS analyses and forecasts of prominent tropical weather systems such as hurricanes. Parallel assimilation experiments with and without rainfall data for Hurricanes Bonnie and Floyd show that assimilating 6-h TMI and SSM/I surfice rain rates leads to more realistic storm features in the analysis, which, in turn, provide better initial conditions for 5-day storm track prediction and precipitation forecast. These results provide evidence that addressing model deficiencies in moisture tendency may be crucial to making effective use of precipitation information in data assimilation.

Hou, Arthur Y.↗
NASA NTRS1979

Future freeze forecasting

Real time GOES thermal data acquisition, an energy balance minimum temperature prediction model and a statistical model are incorporated into a minicomputer system. These components make up the operational "Satellite Freeze Forecast System" being used to aid NOAA, NWS forecasters in developing their freeze forecasts. The general concept of the system is presented in this paper. Specific detailed aspects of the system can be found in the reference cited.

Bartholic, J. F.↗
NASA NTRS1986

Statistical corrections to numerical predictions. IV

The National Meteorological Center Barotropic-Mesh Model has been used to test a statistical correction procedure, designated as M-II, that was developed in Schemm et al. (1981). In the present application, statistical corrections at 12 h resulted in significant reductions of the mean-square errors of both vorticity and the Laplacian of thickness. Predictions to 48 h demonstrated the feasibility of applying corrections at every 12 h in extended forecasts. In addition to these improvements, however, the statistical corrections resulted in a shift of error from smaller to larger-scale motions, improving the smallest scales dramatically but deteriorating the largest scales. This effect is shown to be a consequence of randomization of the residual errors by the regression equations and can be corrected by spatially high-pass filtering the field of corrections before they are applied.

Schemm, Jae-Kyung↗
NASA NTRS2010

An Objective Verification of the North American Mesoscale Model for Kennedy Space Center and Cape Canaveral Air Force Station

The 45th Weather Squadron (45 WS) Launch Weather Officers (LWO's) use the 12-km resolution North American Mesoscale (NAM) model (MesoNAM) text and graphical product forecasts extensively to support launch weather operations. However, the actual performance of the model at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) has not been measured objectively. In order to have tangible evidence of model performance, the 45 WS tasked the Applied Meteorology Unit (AMU; Bauman et ai, 2004) to conduct a detailed statistical analysis of model output compared to observed values. The model products are provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The objective analysis compared the MesoNAM forecast winds, temperature (T) and dew pOint (T d), as well as the changes in these parameters over time, to the observed values from the sensors in the KSC/CCAFS wind tower network shown in Table 1. These objective statistics give the forecasters knowledge of the model's strengths and weaknesses, which will result in improved forecasts for operations.

Bauman, William H., III↗
NASA NTRS2015

Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification

Customer: NASA's Launch Services Program (LSP), Ground Systems Development and Operations (GSDO), and Space Launch System (SLS) programs. NASA's LSP, GSDO, SLS and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). For example, to determine if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 kilometer Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the AMU high-resolution WRF Environmental Modeling System (EMS) model (Watson 2013) in real-time. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The model was set up with a triple-nested grid configuration over KSC/CCAFS based on previous AMU work (Watson 2013). The outer domain (D01) has 12-kilometer grid spacing, the middle domain (D02) has 4-kilometer grid spacing, and the inner domain (D03) has 1.33-kilometer grid spacing. The model runs a 12-hour forecast every hour, D01 and D02 domain outputs are available once an hour and D03 is every 15 minutes during the forecast period. The AMU assessed the WRF-EMS 1.33-kilometer domain model performance for the 2014 warm season (May-September). Verification statistics were computed using the Model Evaluation Tools, which compared the model forecasts to observations. The mean error values were close to 0 and the root mean square error values were less than 1.8 for mean sea-level pressure (millibars), temperature (degrees Kelvin), dewpoint temperature (degrees Kelvin), and wind speed (per millisecond), all very small differences between the forecast and observations considering the normal magnitudes of the parameters. The precipitation forecast verification results showed consistent under-forecasting of the precipitation object size. This could be an artifact of calculating the statistics for each hour rather than for the entire 12-hour period. The AMU will continue to generate verification statistics for the 1.33-kilometer WRF-EMS domain as data become available in future cool and warm seasons. More data will produce more robust statistics and reveal a more accurate assessment of model performance. Once the formal task was complete, the AMU conducted additional work to better understand the wind direction results. The results were stratified diurnally and by wind speed to determine what effects the stratifications would have on the model wind direction verification statistics. The results are summarized in the addendum at the end of this report. In addition to verifying the model's performance, the AMU also made the output available in the Advanced Weather Interactive Processing System II (AWIPS II). This allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations AWIPS II client computers and conduct real-time subjective analyses. In the future, the AMU will implement an updated version of the WRF-EMS model that incorporates local data assimilation. This model will also run in real-time and be made available in AWIPS II.

Numerical Weather Prediction↗