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- Author or Editor: Neil A. Jacobs x
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Abstract
Despite having the largest associated research community and a rapidly growing private sector, the lack of a well-coordinated national research and development effort for U.S. numerical weather prediction continues to impede our ability to utilize more of the scientific and technical capacity of the nation more efficiently. Over the last few years, considerable progress has been made toward developing a community-friendly Unified Forecast System (UFS) by embracing an open innovation approach that is mutually beneficial to the public, private, and academic sectors. Once fully implemented, the UFS has the potential to catalyze a significant increase in the efficacy of our nation’s weather, water, and climate science and prediction.
Abstract
Despite having the largest associated research community and a rapidly growing private sector, the lack of a well-coordinated national research and development effort for U.S. numerical weather prediction continues to impede our ability to utilize more of the scientific and technical capacity of the nation more efficiently. Over the last few years, considerable progress has been made toward developing a community-friendly Unified Forecast System (UFS) by embracing an open innovation approach that is mutually beneficial to the public, private, and academic sectors. Once fully implemented, the UFS has the potential to catalyze a significant increase in the efficacy of our nation’s weather, water, and climate science and prediction.
Abstract
For the first time in over 50 years, the United States has, at the direction of Congress, restructured the way in which federal departments and agencies coordinate to advance meteorological services. The new framework, known as the Interagency Council for Advancing Meteorological Services (ICAMS), encompasses activities spanning local weather to global climate using an Earth system approach. Compared to the previous structure, ICAMS provides a simplified, streamlined framework for coordination across all stakeholders in implementing policies and practices associated with the broad set of services needed by the United States now and into the future. ICAMS also provides improved pathways for research and services integration, as well as mechanisms to more effectively engage the broader community, including academia, industry, nonprofit organizations, and particularly the next generation of educators, researchers, and operational practitioners.
Abstract
For the first time in over 50 years, the United States has, at the direction of Congress, restructured the way in which federal departments and agencies coordinate to advance meteorological services. The new framework, known as the Interagency Council for Advancing Meteorological Services (ICAMS), encompasses activities spanning local weather to global climate using an Earth system approach. Compared to the previous structure, ICAMS provides a simplified, streamlined framework for coordination across all stakeholders in implementing policies and practices associated with the broad set of services needed by the United States now and into the future. ICAMS also provides improved pathways for research and services integration, as well as mechanisms to more effectively engage the broader community, including academia, industry, nonprofit organizations, and particularly the next generation of educators, researchers, and operational practitioners.
Abstract
A method for correcting the magnetic deviation error from planes using a flux valve heading sensor is presented. This error can significantly degrade the quality of the wind data reported from certain commercial airlines. A database is constructed on a per-plane basis and compared to multiple model analyses and observations. A unique filtering method is applied using coefficients derived from this comparison. Three regional airline fleets hosting the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor were analyzed and binned by error statistics. The correction method is applied to the outliers with the largest deviation, and the wind observational error was reduced by 22% (2.4 kt; 1 kt = 0.51 m s−1), 50% (8.2 kt), and 68% (20.5 kt) for each group.
Abstract
A method for correcting the magnetic deviation error from planes using a flux valve heading sensor is presented. This error can significantly degrade the quality of the wind data reported from certain commercial airlines. A database is constructed on a per-plane basis and compared to multiple model analyses and observations. A unique filtering method is applied using coefficients derived from this comparison. Three regional airline fleets hosting the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor were analyzed and binned by error statistics. The correction method is applied to the outliers with the largest deviation, and the wind observational error was reduced by 22% (2.4 kt; 1 kt = 0.51 m s−1), 50% (8.2 kt), and 68% (20.5 kt) for each group.
Abstract
The Atlantic Surface Cyclone Intensification Index (ASCII) is a forecast index that quantifies the strength of low-level baroclinicity in the coastal region of the Carolinas. It is based on the gradient between the coldest 24-h average air temperature at Cape Hatteras and Wilmington, North Carolina, and the temperature at the western boundary of the Gulf Stream. The resulting prestorm baroclinic index (PSBI) is used to forecast the probability that a cyclone in the domain will exhibit rapid cyclogenesis. The initial ASCII study covered the years 1982–90. This dataset was recently expanded to cover the years 1991–2002, which doubled the number of cyclone events in the sample. These additional data provide similar position and slope of the linear regression fits to the previous values, and explain as much as 30% of the variance in cyclone deepening rate.
Despite operational value, the neglect of upper-tropospheric forcing as a predictor in the original ASCII formulation precludes explanation of a large fraction of the deepening rate variance. Here, a modified index is derived in which an approximate measure of upper-level forcing is included. The 1991–2002 cyclone events were separated into bins of “strongly forced,” “moderately forced,” and “weakly forced” based on the strength of the nearest upstream maximum of 500-mb absolute vorticity associated with the surface low. This separation method reduced the scatter and further isolated the contributions of surface forcing versus upper-level forcing on extratropical cyclogenesis. Results of the combined upper-level index and surface PSBI demonstrate that as much as 74% of the deepening rate variance can be explained for cases with stronger upper-level forcing.
Abstract
The Atlantic Surface Cyclone Intensification Index (ASCII) is a forecast index that quantifies the strength of low-level baroclinicity in the coastal region of the Carolinas. It is based on the gradient between the coldest 24-h average air temperature at Cape Hatteras and Wilmington, North Carolina, and the temperature at the western boundary of the Gulf Stream. The resulting prestorm baroclinic index (PSBI) is used to forecast the probability that a cyclone in the domain will exhibit rapid cyclogenesis. The initial ASCII study covered the years 1982–90. This dataset was recently expanded to cover the years 1991–2002, which doubled the number of cyclone events in the sample. These additional data provide similar position and slope of the linear regression fits to the previous values, and explain as much as 30% of the variance in cyclone deepening rate.
Despite operational value, the neglect of upper-tropospheric forcing as a predictor in the original ASCII formulation precludes explanation of a large fraction of the deepening rate variance. Here, a modified index is derived in which an approximate measure of upper-level forcing is included. The 1991–2002 cyclone events were separated into bins of “strongly forced,” “moderately forced,” and “weakly forced” based on the strength of the nearest upstream maximum of 500-mb absolute vorticity associated with the surface low. This separation method reduced the scatter and further isolated the contributions of surface forcing versus upper-level forcing on extratropical cyclogenesis. Results of the combined upper-level index and surface PSBI demonstrate that as much as 74% of the deepening rate variance can be explained for cases with stronger upper-level forcing.
Abstract
A variational bias correction (VarBC) scheme is developed and tested using regional Weather Research and Forecasting Model Data Assimilation (WRFDA) to correct systematic errors in aircraft-based measurements of temperature produced by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) system. Various bias models were investigated, using one or all of aircraft height tendency, Mach number, temperature tendency, and the observed temperature as predictors. These variables were expected to account for the representation of some well-known error sources contributing to uncertainties in TAMDAR temperature measurements. The parameters corresponding to these predictors were evolved in the model for a two-week period to generate initial estimates according to each unique aircraft tail number. Sensitivity experiments were then conducted for another one-month period. Finally, a case study using VarBC of a cold front precipitation event is examined. The implementation of VarBC reduces biases in TAMDAR temperature innovations. Even when using a bias model containing a single predictor, such as height tendency or Mach number, the VarBC produces positive impacts on analyses and short-range forecasts of temperature with smaller standard deviations and biases than the control run. Additionally, by employing a multiple-predictor bias model, which describes the statistical relations between innovations and predictors, and uses coefficients to control the evolution of components in the bias model with respect to their reference values, VarBC further reduces the average error of analyses and short-range forecasts with respect to observations. The potential impacts of VarBC on precipitation forecasts were evaluated, and the VarBC is able to indirectly improve the prediction of precipitation location by reducing the forecast error for wind-related synoptic circulation leading to precipitation.
Abstract
A variational bias correction (VarBC) scheme is developed and tested using regional Weather Research and Forecasting Model Data Assimilation (WRFDA) to correct systematic errors in aircraft-based measurements of temperature produced by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) system. Various bias models were investigated, using one or all of aircraft height tendency, Mach number, temperature tendency, and the observed temperature as predictors. These variables were expected to account for the representation of some well-known error sources contributing to uncertainties in TAMDAR temperature measurements. The parameters corresponding to these predictors were evolved in the model for a two-week period to generate initial estimates according to each unique aircraft tail number. Sensitivity experiments were then conducted for another one-month period. Finally, a case study using VarBC of a cold front precipitation event is examined. The implementation of VarBC reduces biases in TAMDAR temperature innovations. Even when using a bias model containing a single predictor, such as height tendency or Mach number, the VarBC produces positive impacts on analyses and short-range forecasts of temperature with smaller standard deviations and biases than the control run. Additionally, by employing a multiple-predictor bias model, which describes the statistical relations between innovations and predictors, and uses coefficients to control the evolution of components in the bias model with respect to their reference values, VarBC further reduces the average error of analyses and short-range forecasts with respect to observations. The potential impacts of VarBC on precipitation forecasts were evaluated, and the VarBC is able to indirectly improve the prediction of precipitation location by reducing the forecast error for wind-related synoptic circulation leading to precipitation.
Abstract
Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations are becoming a major data source for numerical weather prediction (NWP) because of the advantages of their high spatiotemporal resolution and humidity measurements. In this study, the estimation of TAMDAR observational errors, and the impacts of TAMDAR observations with new error statistics on short-term forecasts are presented. The observational errors are estimated by a three-way collocated statistical comparison. This method employs collocated meteorological reports from three data sources: TAMDAR, radiosondes, and the 6-h forecast from a Weather Research and Forecasting Model (WRF). The performance of TAMDAR observations with the new error statistics was then evaluated based on this model, and the WRF Data Assimilation (WRFDA) three-dimensional variational data assimilation (3DVAR) system. The analysis was conducted for both January and June of 2010. The experiments assimilate TAMDAR, as well as other conventional data with the exception of non-TAMDAR aircraft observations, every 6 h, and a 24-h forecast is produced. The standard deviation of the observational error of TAMDAR, which has relatively stable values regardless of season, is comparable to radiosondes for temperature, and slightly smaller than that of a radiosonde for relative humidity. The observational errors in wind direction significantly depend on wind speeds. In general, at low wind speeds, the error in TAMDAR is greater than that of radiosondes; however, the opposite is true for higher wind speeds. The impact of TAMDAR observations on both the 6- and 24-h WRF forecasts during the studied period is positive when using the default observational aircraft weather report (AIREP) error statistics. The new TAMDAR error statistics presented here bring additional improvement over the default error.
Abstract
Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations are becoming a major data source for numerical weather prediction (NWP) because of the advantages of their high spatiotemporal resolution and humidity measurements. In this study, the estimation of TAMDAR observational errors, and the impacts of TAMDAR observations with new error statistics on short-term forecasts are presented. The observational errors are estimated by a three-way collocated statistical comparison. This method employs collocated meteorological reports from three data sources: TAMDAR, radiosondes, and the 6-h forecast from a Weather Research and Forecasting Model (WRF). The performance of TAMDAR observations with the new error statistics was then evaluated based on this model, and the WRF Data Assimilation (WRFDA) three-dimensional variational data assimilation (3DVAR) system. The analysis was conducted for both January and June of 2010. The experiments assimilate TAMDAR, as well as other conventional data with the exception of non-TAMDAR aircraft observations, every 6 h, and a 24-h forecast is produced. The standard deviation of the observational error of TAMDAR, which has relatively stable values regardless of season, is comparable to radiosondes for temperature, and slightly smaller than that of a radiosonde for relative humidity. The observational errors in wind direction significantly depend on wind speeds. In general, at low wind speeds, the error in TAMDAR is greater than that of radiosondes; however, the opposite is true for higher wind speeds. The impact of TAMDAR observations on both the 6- and 24-h WRF forecasts during the studied period is positive when using the default observational aircraft weather report (AIREP) error statistics. The new TAMDAR error statistics presented here bring additional improvement over the default error.
The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission was recommended by the National Research Council's (NRC's) Earth Science Decadal Survey to measure tropospheric trace gases and aerosols and coastal ocean phytoplankton, water quality, and biogeochemistry from geostationary orbit, providing continuous observations within the field of view. To fulfill the mandate and address the challenge put forth by the NRC, two GEO-CAPE Science Working Groups (SWGs), representing the atmospheric composition and ocean color disciplines, have developed realistic science objectives using input drawn from several community workshops. The GEO-CAPE mission will take advantage of this revolutionary advance in temporal frequency for both of these disciplines. Multiple observations per day are required to explore the physical, chemical, and dynamical processes that determine tropospheric composition and air quality over spatial scales ranging from urban to continental, and over temporal scales ranging from diurnal to seasonal. Likewise, high-frequency satellite observations are critical to studying and quantifying biological, chemical, and physical processes within the coastal ocean. These observations are to be achieved from a vantage point near 95°–100°W, providing a complete view of North America as well as the adjacent oceans. The SWGs have also endorsed the concept of phased implementation using commercial satellites to reduce mission risk and cost. GEO-CAPE will join the global constellation of geostationary atmospheric chemistry and coastal ocean color sensors planned to be in orbit in the 2020 time frame.
The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission was recommended by the National Research Council's (NRC's) Earth Science Decadal Survey to measure tropospheric trace gases and aerosols and coastal ocean phytoplankton, water quality, and biogeochemistry from geostationary orbit, providing continuous observations within the field of view. To fulfill the mandate and address the challenge put forth by the NRC, two GEO-CAPE Science Working Groups (SWGs), representing the atmospheric composition and ocean color disciplines, have developed realistic science objectives using input drawn from several community workshops. The GEO-CAPE mission will take advantage of this revolutionary advance in temporal frequency for both of these disciplines. Multiple observations per day are required to explore the physical, chemical, and dynamical processes that determine tropospheric composition and air quality over spatial scales ranging from urban to continental, and over temporal scales ranging from diurnal to seasonal. Likewise, high-frequency satellite observations are critical to studying and quantifying biological, chemical, and physical processes within the coastal ocean. These observations are to be achieved from a vantage point near 95°–100°W, providing a complete view of North America as well as the adjacent oceans. The SWGs have also endorsed the concept of phased implementation using commercial satellites to reduce mission risk and cost. GEO-CAPE will join the global constellation of geostationary atmospheric chemistry and coastal ocean color sensors planned to be in orbit in the 2020 time frame.
