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The value of climate data and the information derived from the data still seems to be an unknown to many. Five persons engaged in providing climate services in different U.S. climatic zones have assembled a few widely different examples of recent uses of climate data and information. These help demonstrate the diversity of applications, and the value of the data and of those who can interpret them.
The value of climate data and the information derived from the data still seems to be an unknown to many. Five persons engaged in providing climate services in different U.S. climatic zones have assembled a few widely different examples of recent uses of climate data and information. These help demonstrate the diversity of applications, and the value of the data and of those who can interpret them.
Abstract
A multiyear evaluation of a regional aircraft observation system [Tropospheric Aircraft Meteorological Data Reports (TAMDAR)] is presented. TAMDAR observation errors are compared with errors in traditional reports from commercial aircraft [aircraft meteorological data reports (AMDAR)], and the impacts of TAMDAR observations on forecasts from the Rapid Update Cycle (RUC) over a 3-yr period are evaluated. Because of the high vertical resolution of TAMDAR observations near the surface, a novel verification system has been developed and employed that compares RUC forecasts against raobs every 10 hPa; this revealed TAMDAR-related positive impacts on RUC forecasts—particularly for relative humidity forecasts—that were not evident when only raob mandatory levels were considered. In addition, multiple retrospective experiments were performed over two 10-day periods, one in winter and one in summer; these allowed for the assessment of the impacts of various data assimilation strategies and varying data resolutions. TAMDAR’s impacts on 3-h RUC forecasts of temperature, relative humidity, and wind are found to be positive and, for temperature and relative humidity, substantial in the region, altitude, and time range over which TAMDAR-equipped aircraft operated during the studied period of analysis.
Abstract
A multiyear evaluation of a regional aircraft observation system [Tropospheric Aircraft Meteorological Data Reports (TAMDAR)] is presented. TAMDAR observation errors are compared with errors in traditional reports from commercial aircraft [aircraft meteorological data reports (AMDAR)], and the impacts of TAMDAR observations on forecasts from the Rapid Update Cycle (RUC) over a 3-yr period are evaluated. Because of the high vertical resolution of TAMDAR observations near the surface, a novel verification system has been developed and employed that compares RUC forecasts against raobs every 10 hPa; this revealed TAMDAR-related positive impacts on RUC forecasts—particularly for relative humidity forecasts—that were not evident when only raob mandatory levels were considered. In addition, multiple retrospective experiments were performed over two 10-day periods, one in winter and one in summer; these allowed for the assessment of the impacts of various data assimilation strategies and varying data resolutions. TAMDAR’s impacts on 3-h RUC forecasts of temperature, relative humidity, and wind are found to be positive and, for temperature and relative humidity, substantial in the region, altitude, and time range over which TAMDAR-equipped aircraft operated during the studied period of analysis.
Abstract
An assessment is presented on the relative forecast impact on the performance of a numerical weather prediction model from eight different observation data types: aircraft, profiler, radiosonde, velocity azimuth display (VAD), GPS-derived precipitable water, aviation routine weather report (METAR; surface), surface mesonet, and satellite-based atmospheric motion vectors. A series of observation sensitivity experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3–12 h) wind, temperature, and relative humidity forecasts at different vertical levels and near the surface. These experiments were conducted for two 10-day periods, one in November–December 2006 and one in August 2007. These experiments show positive short-range forecast impacts from most of the contributors to the heterogeneous observing system over the RUC domain. In particular, aircraft observations had the largest overall impact for forecasts initialized 3–6 h before 0000 or 1200 UTC, considered over the full depth (1000–100 hPa), followed by radiosonde observations, even though the latter are available only every 12 h. Profiler data (including at a hypothetical 8-km depth), GPS-precipitable water estimates, and surface observations also led to significant improvements in short-range forecast skill.
Abstract
An assessment is presented on the relative forecast impact on the performance of a numerical weather prediction model from eight different observation data types: aircraft, profiler, radiosonde, velocity azimuth display (VAD), GPS-derived precipitable water, aviation routine weather report (METAR; surface), surface mesonet, and satellite-based atmospheric motion vectors. A series of observation sensitivity experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3–12 h) wind, temperature, and relative humidity forecasts at different vertical levels and near the surface. These experiments were conducted for two 10-day periods, one in November–December 2006 and one in August 2007. These experiments show positive short-range forecast impacts from most of the contributors to the heterogeneous observing system over the RUC domain. In particular, aircraft observations had the largest overall impact for forecasts initialized 3–6 h before 0000 or 1200 UTC, considered over the full depth (1000–100 hPa), followed by radiosonde observations, even though the latter are available only every 12 h. Profiler data (including at a hypothetical 8-km depth), GPS-precipitable water estimates, and surface observations also led to significant improvements in short-range forecast skill.
Abstract
This global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.
Abstract
This global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.
A variety of storm top electrical discharges have been observed using several types of low-light imagers, film, and the human eye. Recently, a video recorded an unprecedented, bright blue upward discharge from a tropical thunderstorm top near Puerto Rico. The event reached the base of the ionosphere. The horizontal dimensions of cloud top discharges can range from 100 m to several kilometers. Upward extents vary from 100 m to 70 km. Shapes include “points” of light, upwardly flaring trumpets, and narrow, vertical, lightning-like channels, some topped with expanding blue, flame-like features. Visual appearances range from brilliant white lightning-like channels to a grainy, almost particulate appearing jets of dim blue light, and sometimes as a blue flame within which a brilliant white channel appears. The classical blue jet is at the lower limit of human night vision whereas some upward discharges have been clearly seen during daylight. Cloud top “pixies” last no longer than 16.7 ms, whereas upward lightning-like channels are often characterized as long lasting (2.0 s or more). To date, optical measurements have not associated cloud-top events with specific lightning flashes. There is a strong tendency for all such events to occur above the convective dome of rapidly intensifying thunderstorms. It is possible that the great diversity of forms illustrates the complexity inherent in the upward streamer mechanism for blue jets. It is also possible that the basic blue jet is only one of several distinct classes of discharges from highly electrified storm cloud tops. Future research should focus on rapidly growing convective storm tops, including supercells and intense oceanic storms, as opposed to the stratiform regions of large mesoscale convective systems that have characterized sprite observations to date.
A variety of storm top electrical discharges have been observed using several types of low-light imagers, film, and the human eye. Recently, a video recorded an unprecedented, bright blue upward discharge from a tropical thunderstorm top near Puerto Rico. The event reached the base of the ionosphere. The horizontal dimensions of cloud top discharges can range from 100 m to several kilometers. Upward extents vary from 100 m to 70 km. Shapes include “points” of light, upwardly flaring trumpets, and narrow, vertical, lightning-like channels, some topped with expanding blue, flame-like features. Visual appearances range from brilliant white lightning-like channels to a grainy, almost particulate appearing jets of dim blue light, and sometimes as a blue flame within which a brilliant white channel appears. The classical blue jet is at the lower limit of human night vision whereas some upward discharges have been clearly seen during daylight. Cloud top “pixies” last no longer than 16.7 ms, whereas upward lightning-like channels are often characterized as long lasting (2.0 s or more). To date, optical measurements have not associated cloud-top events with specific lightning flashes. There is a strong tendency for all such events to occur above the convective dome of rapidly intensifying thunderstorms. It is possible that the great diversity of forms illustrates the complexity inherent in the upward streamer mechanism for blue jets. It is also possible that the basic blue jet is only one of several distinct classes of discharges from highly electrified storm cloud tops. Future research should focus on rapidly growing convective storm tops, including supercells and intense oceanic storms, as opposed to the stratiform regions of large mesoscale convective systems that have characterized sprite observations to date.
Over a decade of monitoring mesospheric transient luminous events (TLEs) above U.S. high plains storms confirmed sprites are almost exclusively associated with positive polarity cloud-to-ground lightning (+CGs). Following C. T. R. Wilson's theory proposed in 1925, only those +CGs lowering large amounts of charge to ground should induce sprites. The key metric, the charge moment change, generally must exceed ~600 C km to initiate the electric breakdown at 75 km, which evolves into the sprite. High plains storms generate the highest percentage, the largest average peak current, and highest density of +CGs in the nation. Various storm types generate +CGs, and especially supercells are often dominated by positive strokes. Few sprites observations above supercells have been obtained (and usually during their decaying phase), while thousands of sprites have been imaged above mesoscale convective system (MCS) stratiform regions and some squall lines. During the 2000 Severe Thunderstorm Electrification and Precipitation Study (STEPS), two supercells were examined. One storm contained >90% +CGs, but none exceeded the sprite charge moment change threshold. A second nocturnal supercell did produce sprites from the last two +CGs of the storm as a stratiform region developed, more favorable for significant continuing currents to follow the +CG return stroke. Unexpectedly, three sprites occurring during the most intense phase of the storm were triggered by unusually intense and impulsive +CGs, which lowered sufficient charge in the return stroke alone for sprite initiation. Such +CGs, and thus sprites, are probably relatively rare events during the supercell mature stage.
Over a decade of monitoring mesospheric transient luminous events (TLEs) above U.S. high plains storms confirmed sprites are almost exclusively associated with positive polarity cloud-to-ground lightning (+CGs). Following C. T. R. Wilson's theory proposed in 1925, only those +CGs lowering large amounts of charge to ground should induce sprites. The key metric, the charge moment change, generally must exceed ~600 C km to initiate the electric breakdown at 75 km, which evolves into the sprite. High plains storms generate the highest percentage, the largest average peak current, and highest density of +CGs in the nation. Various storm types generate +CGs, and especially supercells are often dominated by positive strokes. Few sprites observations above supercells have been obtained (and usually during their decaying phase), while thousands of sprites have been imaged above mesoscale convective system (MCS) stratiform regions and some squall lines. During the 2000 Severe Thunderstorm Electrification and Precipitation Study (STEPS), two supercells were examined. One storm contained >90% +CGs, but none exceeded the sprite charge moment change threshold. A second nocturnal supercell did produce sprites from the last two +CGs of the storm as a stratiform region developed, more favorable for significant continuing currents to follow the +CG return stroke. Unexpectedly, three sprites occurring during the most intense phase of the storm were triggered by unusually intense and impulsive +CGs, which lowered sufficient charge in the return stroke alone for sprite initiation. Such +CGs, and thus sprites, are probably relatively rare events during the supercell mature stage.
Abstract
This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis.
Abstract
This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis.
Abstract
A hydrostatic global weather prediction model based on an icosahedral horizontal grid and a hybrid terrain-following/isentropic vertical coordinate is described. The model is an extension to three spatial dimensions of a previously developed, icosahedral, shallow-water model featuring user-selectable horizontal resolution and employing indirect addressing techniques. The vertical grid is adaptive to maximize the portion of the atmosphere mapped into the isentropic coordinate subdomain. The model, best described as a stacked shallow-water model, is being tested extensively on real-time medium-range forecasts to ready it for possible inclusion in operational multimodel ensembles for medium-range to seasonal prediction.
Abstract
A hydrostatic global weather prediction model based on an icosahedral horizontal grid and a hybrid terrain-following/isentropic vertical coordinate is described. The model is an extension to three spatial dimensions of a previously developed, icosahedral, shallow-water model featuring user-selectable horizontal resolution and employing indirect addressing techniques. The vertical grid is adaptive to maximize the portion of the atmosphere mapped into the isentropic coordinate subdomain. The model, best described as a stacked shallow-water model, is being tested extensively on real-time medium-range forecasts to ready it for possible inclusion in operational multimodel ensembles for medium-range to seasonal prediction.
Weather and climatic extremes can have serious and damaging effects on human society and infrastructure as well as on ecosystems and wildlife. Thus, they are usually the main focus of attention of the news media in reports on climate. There are some indications from observations concerning how climatic extremes may have changed in the past. Climate models show how they could change in the future either due to natural climate fluctuations or under conditions of greenhouse gas-induced warming. These observed and modeled changes relate directly to the understanding of socioeconomic and ecological impacts related to extremes.
Weather and climatic extremes can have serious and damaging effects on human society and infrastructure as well as on ecosystems and wildlife. Thus, they are usually the main focus of attention of the news media in reports on climate. There are some indications from observations concerning how climatic extremes may have changed in the past. Climate models show how they could change in the future either due to natural climate fluctuations or under conditions of greenhouse gas-induced warming. These observed and modeled changes relate directly to the understanding of socioeconomic and ecological impacts related to extremes.
Abstract
The Mesoscale Predictability Experiment (MPEX) was conducted from 15 May to 15 June 2013 in the central United States. MPEX was motivated by the basic question of whether experimental, subsynoptic observations can extend convective-scale predictability and otherwise enhance skill in short-term regional numerical weather prediction.
Observational tools for MPEX included the National Science Foundation (NSF)–National Center for Atmospheric Research (NCAR) Gulfstream V aircraft (GV), which featured the Airborne Vertical Atmospheric Profiling System mini-dropsonde system and a microwave temperature-profiling (MTP) system as well as several ground-based mobile upsonde systems. Basic operations involved two missions per day: an early morning mission with the GV, well upstream of anticipated convective storms, and an afternoon and early evening mission with the mobile sounding units to sample the initiation and upscale feedbacks of the convection.
A total of 18 intensive observing periods (IOPs) were completed during the field phase, representing a wide spectrum of synoptic regimes and convective events, including several major severe weather and/or tornado outbreak days. The novel observational strategy employed during MPEX is documented herein, as is the unique role of the ensemble modeling efforts—which included an ensemble sensitivity analysis—to both guide the observational strategies and help address the potential impacts of such enhanced observations on short-term convective forecasting. Preliminary results of retrospective data assimilation experiments are discussed, as are data analyses showing upscale convective feedbacks.
Abstract
The Mesoscale Predictability Experiment (MPEX) was conducted from 15 May to 15 June 2013 in the central United States. MPEX was motivated by the basic question of whether experimental, subsynoptic observations can extend convective-scale predictability and otherwise enhance skill in short-term regional numerical weather prediction.
Observational tools for MPEX included the National Science Foundation (NSF)–National Center for Atmospheric Research (NCAR) Gulfstream V aircraft (GV), which featured the Airborne Vertical Atmospheric Profiling System mini-dropsonde system and a microwave temperature-profiling (MTP) system as well as several ground-based mobile upsonde systems. Basic operations involved two missions per day: an early morning mission with the GV, well upstream of anticipated convective storms, and an afternoon and early evening mission with the mobile sounding units to sample the initiation and upscale feedbacks of the convection.
A total of 18 intensive observing periods (IOPs) were completed during the field phase, representing a wide spectrum of synoptic regimes and convective events, including several major severe weather and/or tornado outbreak days. The novel observational strategy employed during MPEX is documented herein, as is the unique role of the ensemble modeling efforts—which included an ensemble sensitivity analysis—to both guide the observational strategies and help address the potential impacts of such enhanced observations on short-term convective forecasting. Preliminary results of retrospective data assimilation experiments are discussed, as are data analyses showing upscale convective feedbacks.
