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Abstract
The dual-polarization (dual pol) Doppler radar can transmit/receive both horizontally and vertically polarized power returns. The dual-pol radar measurements have been shown to provide a more accurate precipitation estimate compared to traditional radars. In this study, the horizontal reflectivity ZH , differential reflectivity Z DR, specific differential phase K DP, and radial velocity VR collected by the C-band Advanced Radar for Meteorological and Operational Research (ARMOR) are assimilated for two convective storms. A warm-rain scheme is constructed to assimilate ZH , Z DR, and K DP data using the three-dimensional variational data assimilation (3DVAR) system with the Advanced Research Weather Research and Forecasting Model (ARW-WRF). The main goals of this study are first to demonstrate and compare the impact of various dual-pol variables in initialization of real case convective storms and second to test how the dual-pol fields may be better used with a 3DVAR system.
The results show that the ZH , Z DR, K DP, and VR data substantially improve the initial condition for two mesoscale convective storms. Significant positive impacts on short-term forecast are obtained for both storms. Additionally, K DP and Z DR data assimilation is shown to be superior to ZH and Z DR and ZH -only data assimilation when the warm-rain microphysics is adopted. With the ongoing upgrade of the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network to include dual-pol capabilities (started in early 2011), the findings from this study can be a helpful reference for utilizing the dual-pol radar data in numerical simulations of severe weather and related quantitative precipitation forecasts.
Abstract
The dual-polarization (dual pol) Doppler radar can transmit/receive both horizontally and vertically polarized power returns. The dual-pol radar measurements have been shown to provide a more accurate precipitation estimate compared to traditional radars. In this study, the horizontal reflectivity ZH , differential reflectivity Z DR, specific differential phase K DP, and radial velocity VR collected by the C-band Advanced Radar for Meteorological and Operational Research (ARMOR) are assimilated for two convective storms. A warm-rain scheme is constructed to assimilate ZH , Z DR, and K DP data using the three-dimensional variational data assimilation (3DVAR) system with the Advanced Research Weather Research and Forecasting Model (ARW-WRF). The main goals of this study are first to demonstrate and compare the impact of various dual-pol variables in initialization of real case convective storms and second to test how the dual-pol fields may be better used with a 3DVAR system.
The results show that the ZH , Z DR, K DP, and VR data substantially improve the initial condition for two mesoscale convective storms. Significant positive impacts on short-term forecast are obtained for both storms. Additionally, K DP and Z DR data assimilation is shown to be superior to ZH and Z DR and ZH -only data assimilation when the warm-rain microphysics is adopted. With the ongoing upgrade of the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network to include dual-pol capabilities (started in early 2011), the findings from this study can be a helpful reference for utilizing the dual-pol radar data in numerical simulations of severe weather and related quantitative precipitation forecasts.
Abstract
A convective cloud (CC) analysis is performed over the southeastern United States (SEUS) during June, July, and August 2006 and 2007, using data from the Geostationary Operational Environmental Satellite (GOES) visible and infrared sensors as processed by a satellite-based convection cloud mask and initiation algorithm. Six 5–7-day periods are analyzed between the times 1500 and 1900 UTC, representative of summertime conditions in the SEUS. The ~8.7 × 108 pixel database contains information on nonprecipitating CCs possessing various satellite-estimated attributes of cloud size, based on whether they meet set thresholds in eight infrared “interest fields.” CCs at ~1 km × 1 km pixel size in the GOES projection are evaluated in comparison with the land cover classes, elevation gradients, and normalized difference vegetation indices (NDVIs) beneath the CCs. The goals are to relate the frequency of occurrence of CCs to land surface properties, attempting to determine which of these three properties are most correlated with CCs. CCs are more likely to form over forests and dense vegetation and over higher gradients in elevation. Although forest cover classes are not the most common over the SEUS, CC occurrence increases disproportionately where steeply sloped topography and forests are coincident across large regions of the SEUS. Also, as NDVI increases, the percentage of CCs per land class also increases. Analysis of landscape heterogeneity (combining local variability in land classes, topography, and NDVI) shows that as it increases CC development is more widespread. Thus, lakes among forests and hilly topography intermingled with agricultural lands appear most conducive to high CC frequency.
Abstract
A convective cloud (CC) analysis is performed over the southeastern United States (SEUS) during June, July, and August 2006 and 2007, using data from the Geostationary Operational Environmental Satellite (GOES) visible and infrared sensors as processed by a satellite-based convection cloud mask and initiation algorithm. Six 5–7-day periods are analyzed between the times 1500 and 1900 UTC, representative of summertime conditions in the SEUS. The ~8.7 × 108 pixel database contains information on nonprecipitating CCs possessing various satellite-estimated attributes of cloud size, based on whether they meet set thresholds in eight infrared “interest fields.” CCs at ~1 km × 1 km pixel size in the GOES projection are evaluated in comparison with the land cover classes, elevation gradients, and normalized difference vegetation indices (NDVIs) beneath the CCs. The goals are to relate the frequency of occurrence of CCs to land surface properties, attempting to determine which of these three properties are most correlated with CCs. CCs are more likely to form over forests and dense vegetation and over higher gradients in elevation. Although forest cover classes are not the most common over the SEUS, CC occurrence increases disproportionately where steeply sloped topography and forests are coincident across large regions of the SEUS. Also, as NDVI increases, the percentage of CCs per land class also increases. Analysis of landscape heterogeneity (combining local variability in land classes, topography, and NDVI) shows that as it increases CC development is more widespread. Thus, lakes among forests and hilly topography intermingled with agricultural lands appear most conducive to high CC frequency.
Abstract
This study identifies the precursor signals of convective initiation within sequences of 1-km-resolution visible (VIS) and 4–8-km infrared (IR) imagery from the Geostationary Operational Environmental Satellite (GOES) instrument. Convective initiation (CI) is defined for this study as the first detection of Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivities ≥35 dBZ produced by convective clouds. Results indicate that CI may be forecasted ∼30–45 min in advance through the monitoring of key IR fields for convective clouds. This is made possible by the coincident use of three components of GOES data: 1) a cumulus cloud “mask” at 1-km resolution using VIS and IR data, 2) satellite-derived atmospheric motion vectors (AMVs) for tracking individual cumulus clouds, and 3) IR brightness temperature (TB ) and multispectral band-differencing time trends. In effect, these techniques isolate only the cumulus convection in satellite imagery, track moving cumulus convection, and evaluate various IR cloud properties in time. Convective initiation is predicted by accumulating information within a satellite pixel that is attributed to the first occurrence of a ≥35 dBZ radar echo. Through the incorporation of satellite tracking of moving cumulus clouds, this work represents a significant advance in the use of routinely available GOES data for monitoring aspects of cumulus clouds important for nowcasting CI (0–1-h forecasts). Once cumulus cloud tracking is established, eight predictor fields based on Lagrangian trends in IR data are used to characterize cloud conditions consistent with CI. Cumulus cloud pixels for which ≥7 of the 8 CI indicators are satisfied are labeled as having high CI potential, assuming an extrapolation of past trends into the future. Comparison to future WSR-88D imagery then measures the method's predictive skill. Convective initiation predictability is demonstrated using several convective events—one during IHOP_2002—that occur over a variety of synoptic and mesoscale forcing regimes.
Abstract
This study identifies the precursor signals of convective initiation within sequences of 1-km-resolution visible (VIS) and 4–8-km infrared (IR) imagery from the Geostationary Operational Environmental Satellite (GOES) instrument. Convective initiation (CI) is defined for this study as the first detection of Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivities ≥35 dBZ produced by convective clouds. Results indicate that CI may be forecasted ∼30–45 min in advance through the monitoring of key IR fields for convective clouds. This is made possible by the coincident use of three components of GOES data: 1) a cumulus cloud “mask” at 1-km resolution using VIS and IR data, 2) satellite-derived atmospheric motion vectors (AMVs) for tracking individual cumulus clouds, and 3) IR brightness temperature (TB ) and multispectral band-differencing time trends. In effect, these techniques isolate only the cumulus convection in satellite imagery, track moving cumulus convection, and evaluate various IR cloud properties in time. Convective initiation is predicted by accumulating information within a satellite pixel that is attributed to the first occurrence of a ≥35 dBZ radar echo. Through the incorporation of satellite tracking of moving cumulus clouds, this work represents a significant advance in the use of routinely available GOES data for monitoring aspects of cumulus clouds important for nowcasting CI (0–1-h forecasts). Once cumulus cloud tracking is established, eight predictor fields based on Lagrangian trends in IR data are used to characterize cloud conditions consistent with CI. Cumulus cloud pixels for which ≥7 of the 8 CI indicators are satisfied are labeled as having high CI potential, assuming an extrapolation of past trends into the future. Comparison to future WSR-88D imagery then measures the method's predictive skill. Convective initiation predictability is demonstrated using several convective events—one during IHOP_2002—that occur over a variety of synoptic and mesoscale forcing regimes.
Abstract
This study demonstrates methods to obtain high-density, satellite-derived atmospheric motion vectors (AMV) that contain both synoptic-scale and mesoscale flow components associated with and induced by cumuliform clouds through adjustments made to the University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) AMV processing algorithm. Operational AMV processing is geared toward the identification of synoptic-scale motions in geostrophic balance, which are useful in data assimilation applications. AMVs identified in the vicinity of deep convection are often rejected by quality-control checks used in the production of operational AMV datasets. Few users of these data have considered the use of AMVs with ageostrophic flow components, which often fail checks that assure both spatial coherence between neighboring AMVs and a strong correlation to an NWP-model first-guess wind field. The UW-CIMSS algorithm identifies coherent cloud and water vapor features (i.e., targets) that can be tracked within a sequence of geostationary visible (VIS) and infrared (IR) imagery. AMVs are derived through the combined use of satellite feature tracking and an NWP-model first guess. Reducing the impact of the NWP-model first guess on the final AMV field, in addition to adjusting the target selection and vector-editing schemes, is found to result in greater than a 20-fold increase in the number of AMVs obtained from the UW-CIMSS algorithm for one convective storm case examined here. Over a three-image sequence of Geostationary Operational Environmental Satellite (GOES)-12 VIS and IR data, 3516 AMVs are obtained, most of which contain flow components that deviate considerably from geostrophy. In comparison, 152 AMVs are derived when a tighter NWP-model constraint and no targeting adjustments were imposed, similar to settings used with operational AMV production algorithms. A detailed analysis reveals that many of these 3516 vectors contain low-level (100–70 kPa) convergent and midlevel (70–40 kPa) to upper-level (40–10 kPa) divergent motion components consistent with localized mesoscale flow patterns. The applicability of AMVs for estimating cloud-top cooling rates at the 1-km pixel scale is demonstrated with excellent correspondence to rates identified by a human expert.
Abstract
This study demonstrates methods to obtain high-density, satellite-derived atmospheric motion vectors (AMV) that contain both synoptic-scale and mesoscale flow components associated with and induced by cumuliform clouds through adjustments made to the University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) AMV processing algorithm. Operational AMV processing is geared toward the identification of synoptic-scale motions in geostrophic balance, which are useful in data assimilation applications. AMVs identified in the vicinity of deep convection are often rejected by quality-control checks used in the production of operational AMV datasets. Few users of these data have considered the use of AMVs with ageostrophic flow components, which often fail checks that assure both spatial coherence between neighboring AMVs and a strong correlation to an NWP-model first-guess wind field. The UW-CIMSS algorithm identifies coherent cloud and water vapor features (i.e., targets) that can be tracked within a sequence of geostationary visible (VIS) and infrared (IR) imagery. AMVs are derived through the combined use of satellite feature tracking and an NWP-model first guess. Reducing the impact of the NWP-model first guess on the final AMV field, in addition to adjusting the target selection and vector-editing schemes, is found to result in greater than a 20-fold increase in the number of AMVs obtained from the UW-CIMSS algorithm for one convective storm case examined here. Over a three-image sequence of Geostationary Operational Environmental Satellite (GOES)-12 VIS and IR data, 3516 AMVs are obtained, most of which contain flow components that deviate considerably from geostrophy. In comparison, 152 AMVs are derived when a tighter NWP-model constraint and no targeting adjustments were imposed, similar to settings used with operational AMV production algorithms. A detailed analysis reveals that many of these 3516 vectors contain low-level (100–70 kPa) convergent and midlevel (70–40 kPa) to upper-level (40–10 kPa) divergent motion components consistent with localized mesoscale flow patterns. The applicability of AMVs for estimating cloud-top cooling rates at the 1-km pixel scale is demonstrated with excellent correspondence to rates identified by a human expert.
Abstract
Tropical plumes are identified in satellite data as elongated cloud bands originating from convective activity along the intertropical convergence zone (ITCZ), often extending far into the subtropics and middle latitudes. Many previous studies consider tropical plumes as a product of quasigeostrophic or convergent forcing. Here the authors consider the view that a tropical plume is the upper branch of an enhanced thermally direct circulation driven by latent heat released along the ITCZ. In this way, tropical plume formation is strongly tied to deep cumulus convection and inertial processes.
Observations of plume development show that as a midlatitude wave nears a subsequent plume genesis region, a northward advection of upper-tropospheric, low potential vorticity (potential vorticity unit ≪ 1) occurs as anticyclonic flow intensifies southeast of the midlatitude wave. As this low potential vorticity (PV) ridges over and straddles the ITCZ, plume genesis occurs. Plume development occurs about 1–2 days prior to the midlatitude wave’s more direct impact on the ITCZ environment as it moves to within 5°–10° of the ITCZ. However, as the midlatitude wave nears the ITCZ, an equatorward advection of high PV occurs to end plume development. Thus, a midlatitude wave both indirectly causes tropical plume formation and appears directly responsible for plume demise.
As the low PV advects across the ITCZ, the meridional inertial stability gradient equilibrates. Under these conditions, it is hypothesized that the work requirements of deep ITCZ convection to spread its outflow and force compensating subsidence ease as inertial stability lowers. In the event that convection transports easterly boundary layer momentum to a level of strong convective outflow, it is found that regions poleward of the ITCZ become dynamically preferred for outflow as convectively generated (negative) PV lowers inertial stability there more than equatorward. Thus, convective-scale processes are suggested to be critical to plume formation.
The diagnostic parameter “inertial available kinetic energy” (IAKE), computed on the 340-K isentrope surface, reveals much reduced inertial stability as PV lowers across the ITCZ in conjunction with tropical plume formation. With an easterly (downgradient for the ITCZ environment) convective momentum transport, IAKE becomes positive in the poleward direction in the plume genesis region, suggesting an inertial instability relative to convective updrafts. Theoretically, ITCZ convection in these instances may use convective available potential energy in the presence of IAKE to explosively develop, forming a tropical plume.
Abstract
Tropical plumes are identified in satellite data as elongated cloud bands originating from convective activity along the intertropical convergence zone (ITCZ), often extending far into the subtropics and middle latitudes. Many previous studies consider tropical plumes as a product of quasigeostrophic or convergent forcing. Here the authors consider the view that a tropical plume is the upper branch of an enhanced thermally direct circulation driven by latent heat released along the ITCZ. In this way, tropical plume formation is strongly tied to deep cumulus convection and inertial processes.
Observations of plume development show that as a midlatitude wave nears a subsequent plume genesis region, a northward advection of upper-tropospheric, low potential vorticity (potential vorticity unit ≪ 1) occurs as anticyclonic flow intensifies southeast of the midlatitude wave. As this low potential vorticity (PV) ridges over and straddles the ITCZ, plume genesis occurs. Plume development occurs about 1–2 days prior to the midlatitude wave’s more direct impact on the ITCZ environment as it moves to within 5°–10° of the ITCZ. However, as the midlatitude wave nears the ITCZ, an equatorward advection of high PV occurs to end plume development. Thus, a midlatitude wave both indirectly causes tropical plume formation and appears directly responsible for plume demise.
As the low PV advects across the ITCZ, the meridional inertial stability gradient equilibrates. Under these conditions, it is hypothesized that the work requirements of deep ITCZ convection to spread its outflow and force compensating subsidence ease as inertial stability lowers. In the event that convection transports easterly boundary layer momentum to a level of strong convective outflow, it is found that regions poleward of the ITCZ become dynamically preferred for outflow as convectively generated (negative) PV lowers inertial stability there more than equatorward. Thus, convective-scale processes are suggested to be critical to plume formation.
The diagnostic parameter “inertial available kinetic energy” (IAKE), computed on the 340-K isentrope surface, reveals much reduced inertial stability as PV lowers across the ITCZ in conjunction with tropical plume formation. With an easterly (downgradient for the ITCZ environment) convective momentum transport, IAKE becomes positive in the poleward direction in the plume genesis region, suggesting an inertial instability relative to convective updrafts. Theoretically, ITCZ convection in these instances may use convective available potential energy in the presence of IAKE to explosively develop, forming a tropical plume.
Abstract
The Department of Energy Atmospheric Radiation Measurement Program has funded the development and installation of five atmospheric emitted radiance interferometer (AERI) systems around the Southern Great Plains Cloud and Radiation Test Bed located in Oklahoma and Kansas. The AERI instruments measure atmospheric emitted radiance to within 1% ambient radiance at 1 cm–1 spectral resolution from 520 to 3000 cm–1 (3–20 μm) at 10-min temporal resolution. This high-spectral-resolution radiance information is inverted through a form of the infrared radiative transfer equation to produce temperature and water vapor profiles within the planetary boundary layer (to 3 km), effectively mapping the thermodynamic state of the lower troposphere. Taking advantage of the 10-min resolution of the AERI thermodynamic profiles, the convective destabilization during the 3 May 1999 Oklahoma–Kansas tornado outbreak is analyzed. Tropospheric changes involving the rapid (on the order of 1–2 h) dissipation of a capping temperature inversion within the planetary boundary layer, increasing boundary layer moisture, and a strong upper-level short wave lead to the systematic development of severe convection on this day. The AERI systems were able to monitor the trends in bulk atmospheric stability via diagnosed quantities such as surface-based parcel equivalent potential temperature, inversion intensity, convective available potential energy, and convective inhibition. The high temporal resolution of temperature and moisture profiling and bulk stability information is unique. Special radiosonde launches (nonsynoptic) are currently the only widely used means to determine this stability information. The array of five AERI instruments within Oklahoma and Kansas (collocated with wind profilers) offers the operational forecaster a unique and important data source for the thermodynamic evolution of the boundary layer, convective instability, and numerical weather prediction model validation.
Abstract
The Department of Energy Atmospheric Radiation Measurement Program has funded the development and installation of five atmospheric emitted radiance interferometer (AERI) systems around the Southern Great Plains Cloud and Radiation Test Bed located in Oklahoma and Kansas. The AERI instruments measure atmospheric emitted radiance to within 1% ambient radiance at 1 cm–1 spectral resolution from 520 to 3000 cm–1 (3–20 μm) at 10-min temporal resolution. This high-spectral-resolution radiance information is inverted through a form of the infrared radiative transfer equation to produce temperature and water vapor profiles within the planetary boundary layer (to 3 km), effectively mapping the thermodynamic state of the lower troposphere. Taking advantage of the 10-min resolution of the AERI thermodynamic profiles, the convective destabilization during the 3 May 1999 Oklahoma–Kansas tornado outbreak is analyzed. Tropospheric changes involving the rapid (on the order of 1–2 h) dissipation of a capping temperature inversion within the planetary boundary layer, increasing boundary layer moisture, and a strong upper-level short wave lead to the systematic development of severe convection on this day. The AERI systems were able to monitor the trends in bulk atmospheric stability via diagnosed quantities such as surface-based parcel equivalent potential temperature, inversion intensity, convective available potential energy, and convective inhibition. The high temporal resolution of temperature and moisture profiling and bulk stability information is unique. Special radiosonde launches (nonsynoptic) are currently the only widely used means to determine this stability information. The array of five AERI instruments within Oklahoma and Kansas (collocated with wind profilers) offers the operational forecaster a unique and important data source for the thermodynamic evolution of the boundary layer, convective instability, and numerical weather prediction model validation.
Abstract
Severe thunderstorms routinely exhibit adjacent maxima and minima in cloud-top vertical vorticity (CTV) downstream of overshooting tops within flow fields retrieved using sequences of fine-temporal-resolution (1-min) Geostationary Operational Environmental Satellite (GOES)-R series imagery. Little is known about the origin of this so-called CTV couplet signature, and whether the signature is the result of flow-field derivational artifacts. Thus, the CTV signature’s relevance to research and operations is currently ambiguous. Within this study, we explore the origin of near-cloud-top rotation using an idealized supercell numerical model simulation. Employing an advanced dense optical flow algorithm, image stereoscopy, and numerical model background wind approximations, the artifacts common with cloud-top flow-field derivation are removed from two supercell case studies sampled by GOES-R imagers. It is demonstrated that the CTV couplet originates from tilted and converged horizontal vorticity that is baroclinically generated in the upper levels (above 10 km) immediately downstream of the overshooting top. This baroclinic generation would not be possible without a strong and sustained updraft, implying an indirect relationship to rotationally maintained supercells. Furthermore, it is demonstrated that CTV couplets derived with optical flow algorithms originate from actual rotation within the storm anvils in the case studies explored here, though supercells with opaque above-anvil cirrus plumes and strong anvil-level negative vertical wind shear may produce rotation signals as an artifact without quality control. Artifact identification and quality control is discussed further here for future research and operations use.
Abstract
Severe thunderstorms routinely exhibit adjacent maxima and minima in cloud-top vertical vorticity (CTV) downstream of overshooting tops within flow fields retrieved using sequences of fine-temporal-resolution (1-min) Geostationary Operational Environmental Satellite (GOES)-R series imagery. Little is known about the origin of this so-called CTV couplet signature, and whether the signature is the result of flow-field derivational artifacts. Thus, the CTV signature’s relevance to research and operations is currently ambiguous. Within this study, we explore the origin of near-cloud-top rotation using an idealized supercell numerical model simulation. Employing an advanced dense optical flow algorithm, image stereoscopy, and numerical model background wind approximations, the artifacts common with cloud-top flow-field derivation are removed from two supercell case studies sampled by GOES-R imagers. It is demonstrated that the CTV couplet originates from tilted and converged horizontal vorticity that is baroclinically generated in the upper levels (above 10 km) immediately downstream of the overshooting top. This baroclinic generation would not be possible without a strong and sustained updraft, implying an indirect relationship to rotationally maintained supercells. Furthermore, it is demonstrated that CTV couplets derived with optical flow algorithms originate from actual rotation within the storm anvils in the case studies explored here, though supercells with opaque above-anvil cirrus plumes and strong anvil-level negative vertical wind shear may produce rotation signals as an artifact without quality control. Artifact identification and quality control is discussed further here for future research and operations use.
Abstract
A retrieval of available water fraction ( f AW) is proposed using surface flux estimates from satellite-based thermal infrared (TIR) imagery and the Atmosphere–Land Exchange Inversion (ALEXI) model. Available water serves as a proxy for soil moisture conditions, where f AW can be converted to volumetric soil moisture through two soil texture dependents parameters—field capacity and permanent wilting point. The ability of ALEXI to provide valuable information about the partitioning of the surface energy budget, which can be largely dictated by soil moisture conditions, accommodates the retrieval of an average f AW over the surface to the rooting depth of the active vegetation. For this method, the fraction of actual to potential evapotranspiration ( f PET) is computed from an ALEXI estimate of latent heat flux and potential evapotranspiration (PET). The ALEXI-estimated f PET can be related to f AW in the soil profile. Four unique f PET to f AW relationships are proposed and validated against Oklahoma Mesonet soil moisture observations within a series of composite periods during the warm seasons of 2002–04. Using the validation results, the most representative of the four relationships is chosen and shown to produce reasonable (mean absolute errors values less than 20%) f AW estimates when compared to Oklahoma Mesonet observations. Quantitative comparisons between ALEXI and modeled f AW estimates from the Eta Data Assimilation System (EDAS) are also performed to assess the possible advantages of using ALEXI soil moisture estimates within numerical weather predication (NWP) simulations. This TIR retrieval technique is advantageous over microwave techniques because of the ability to indirectly sense f AW—and hence soil moisture conditions—extending into the root-zone layer. Retrievals are also possible over dense vegetation cover and are available on spatial resolutions on the order of the native TIR imagery. A notable disadvantage is the inability to retrieve f AW conditions through cloud cover.
Abstract
A retrieval of available water fraction ( f AW) is proposed using surface flux estimates from satellite-based thermal infrared (TIR) imagery and the Atmosphere–Land Exchange Inversion (ALEXI) model. Available water serves as a proxy for soil moisture conditions, where f AW can be converted to volumetric soil moisture through two soil texture dependents parameters—field capacity and permanent wilting point. The ability of ALEXI to provide valuable information about the partitioning of the surface energy budget, which can be largely dictated by soil moisture conditions, accommodates the retrieval of an average f AW over the surface to the rooting depth of the active vegetation. For this method, the fraction of actual to potential evapotranspiration ( f PET) is computed from an ALEXI estimate of latent heat flux and potential evapotranspiration (PET). The ALEXI-estimated f PET can be related to f AW in the soil profile. Four unique f PET to f AW relationships are proposed and validated against Oklahoma Mesonet soil moisture observations within a series of composite periods during the warm seasons of 2002–04. Using the validation results, the most representative of the four relationships is chosen and shown to produce reasonable (mean absolute errors values less than 20%) f AW estimates when compared to Oklahoma Mesonet observations. Quantitative comparisons between ALEXI and modeled f AW estimates from the Eta Data Assimilation System (EDAS) are also performed to assess the possible advantages of using ALEXI soil moisture estimates within numerical weather predication (NWP) simulations. This TIR retrieval technique is advantageous over microwave techniques because of the ability to indirectly sense f AW—and hence soil moisture conditions—extending into the root-zone layer. Retrievals are also possible over dense vegetation cover and are available on spatial resolutions on the order of the native TIR imagery. A notable disadvantage is the inability to retrieve f AW conditions through cloud cover.
Abstract
Ninety-four outflow boundary (OB) collisions were documented in north-central Alabama over the summers of 2005–07 using the Advanced Radar for Meteorological and Operational Research (ARMOR) dual-polarimetric radar located at the Huntsville, Alabama, airport. These data were used to extend and verify previous research and to look for new correlations among the various factors that lead to convective initiation (CI) from OB collisions more frequently. For this study, CI is defined as the first occurrence of a ≥35-dBZ radar echo at an elevation angle of 0.8° and within 10 km of the point of collision, from a convective cloud. The radar reflectivity and angle of collision between both OBs along with time of day at which CI occurs most often were analyzed. Also, the presence of cumulus clouds along either/both OBs, or within the area of collision, was examined using Geostationary Operational Environmental Satellite-12 (GOES-12) visible imagery. A more detailed analysis of 23 of the 94 OBs that passed over the Mobile Integrated Profiling System instruments examines the relation among radar reflectivity, updraft magnitude, and water vapor enhancements. This analysis indicates that OB updraft magnitude is positively correlated with OB reflectivity factor. The main findings are that when OBs collide in a more head-on manner, when both colliding OBs have radar reflectivity values of 15 dBZ or greater, or when cumulus clouds preexist along at least one OB, CI is produced at a greater rate. These results, using a much larger dataset than had previously been used for colliding OBs, are subsequently compared with two existing studies.
Abstract
Ninety-four outflow boundary (OB) collisions were documented in north-central Alabama over the summers of 2005–07 using the Advanced Radar for Meteorological and Operational Research (ARMOR) dual-polarimetric radar located at the Huntsville, Alabama, airport. These data were used to extend and verify previous research and to look for new correlations among the various factors that lead to convective initiation (CI) from OB collisions more frequently. For this study, CI is defined as the first occurrence of a ≥35-dBZ radar echo at an elevation angle of 0.8° and within 10 km of the point of collision, from a convective cloud. The radar reflectivity and angle of collision between both OBs along with time of day at which CI occurs most often were analyzed. Also, the presence of cumulus clouds along either/both OBs, or within the area of collision, was examined using Geostationary Operational Environmental Satellite-12 (GOES-12) visible imagery. A more detailed analysis of 23 of the 94 OBs that passed over the Mobile Integrated Profiling System instruments examines the relation among radar reflectivity, updraft magnitude, and water vapor enhancements. This analysis indicates that OB updraft magnitude is positively correlated with OB reflectivity factor. The main findings are that when OBs collide in a more head-on manner, when both colliding OBs have radar reflectivity values of 15 dBZ or greater, or when cumulus clouds preexist along at least one OB, CI is produced at a greater rate. These results, using a much larger dataset than had previously been used for colliding OBs, are subsequently compared with two existing studies.
Abstract
The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.
Abstract
The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.
