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- Author or Editor: P. E. Kirstetter x
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Abstract
Precipitation events in which rainfall is generated primarily below the freezing level via warm-rain processes have traditionally presented a significant challenge for radar and satellite quantitative precipitation estimation (QPE) algorithms. It is possible to improve QPE in warm-rain events if they are correctly identified/classified as warm rain prior to precipitation estimation. Additionally, it is anticipated that classification schemes incorporating polarimetric radar data will be able to leverage precipitation microphysical information to better identify warm-rain precipitation events. This study lays the groundwork for the development of a polarimetric warm-rain classification algorithm by documenting the typical three-dimensional polarimetric characteristics associated with midlatitude warm-rain precipitation events. These characteristics are then compared with those observed in non-warm-rain events. Nearly all warm-rain precipitation events were characterized by lower median values of Z, Z DR, and K DP relative to the non-warm-rain convective cases. Furthermore, droplet coalescence was determined to be the dominant microphysical process in the majority of warm-rain events, while in non-warm-rain stratiform events, evaporation and breakup appeared to be the dominant (warm) microphysical processes. Most warm-rain events were also associated with sharp decreases in reflectivity, with height above the freezing level coincident with low echo-top heights and freezing-level Z DR values near 0, indicating limited ice- and mixed-phase precipitation growth processes. These results support the feasibility of a future polarimetric warm-rain identification algorithm.
Abstract
Precipitation events in which rainfall is generated primarily below the freezing level via warm-rain processes have traditionally presented a significant challenge for radar and satellite quantitative precipitation estimation (QPE) algorithms. It is possible to improve QPE in warm-rain events if they are correctly identified/classified as warm rain prior to precipitation estimation. Additionally, it is anticipated that classification schemes incorporating polarimetric radar data will be able to leverage precipitation microphysical information to better identify warm-rain precipitation events. This study lays the groundwork for the development of a polarimetric warm-rain classification algorithm by documenting the typical three-dimensional polarimetric characteristics associated with midlatitude warm-rain precipitation events. These characteristics are then compared with those observed in non-warm-rain events. Nearly all warm-rain precipitation events were characterized by lower median values of Z, Z DR, and K DP relative to the non-warm-rain convective cases. Furthermore, droplet coalescence was determined to be the dominant microphysical process in the majority of warm-rain events, while in non-warm-rain stratiform events, evaporation and breakup appeared to be the dominant (warm) microphysical processes. Most warm-rain events were also associated with sharp decreases in reflectivity, with height above the freezing level coincident with low echo-top heights and freezing-level Z DR values near 0, indicating limited ice- and mixed-phase precipitation growth processes. These results support the feasibility of a future polarimetric warm-rain identification algorithm.
Abstract
Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.
Abstract
Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.
Abstract
Quantitative precipitation estimation (QPE) products from the next-generation National Mosaic and QPE system (Q2) are cross-compared to the operational, radar-only product of the National Weather Service (Stage II) using the gauge-adjusted and manual quality-controlled product (Stage IV) as a reference. The evaluation takes place over the entire conterminous United States (CONUS) from December 2009 to November 2010. The annual comparison of daily Stage II precipitation to the radar-only Q2Rad product indicates that both have small systematic biases (absolute values > 8%), but the random errors with Stage II are much greater, as noted with a root-mean-squared difference of 4.5 mm day−1 compared to 1.1 mm day−1 with Q2Rad and a lower correlation coefficient (0.20 compared to 0.73). The Q2 logic of identifying precipitation types as being convective, stratiform, or tropical at each grid point and applying differential Z–R equations has been successful in removing regional biases (i.e., overestimated rainfall from Stage II east of the Appalachians) and greatly diminishes seasonal bias patterns that were found with Stage II. Biases and radar artifacts along the coastal mountain and intermountain chains were not mitigated with rain gauge adjustment and thus require new approaches by the community. The evaluation identifies a wet bias by Q2Rad in the central plains and the South and then introduces intermediate products to explain it. Finally, this study provides estimates of uncertainty using the radar quality index product for both Q2Rad and the gauge-corrected Q2RadGC daily precipitation products. This error quantification should be useful to the satellite QPE community who use Q2 products as a reference.
Abstract
Quantitative precipitation estimation (QPE) products from the next-generation National Mosaic and QPE system (Q2) are cross-compared to the operational, radar-only product of the National Weather Service (Stage II) using the gauge-adjusted and manual quality-controlled product (Stage IV) as a reference. The evaluation takes place over the entire conterminous United States (CONUS) from December 2009 to November 2010. The annual comparison of daily Stage II precipitation to the radar-only Q2Rad product indicates that both have small systematic biases (absolute values > 8%), but the random errors with Stage II are much greater, as noted with a root-mean-squared difference of 4.5 mm day−1 compared to 1.1 mm day−1 with Q2Rad and a lower correlation coefficient (0.20 compared to 0.73). The Q2 logic of identifying precipitation types as being convective, stratiform, or tropical at each grid point and applying differential Z–R equations has been successful in removing regional biases (i.e., overestimated rainfall from Stage II east of the Appalachians) and greatly diminishes seasonal bias patterns that were found with Stage II. Biases and radar artifacts along the coastal mountain and intermountain chains were not mitigated with rain gauge adjustment and thus require new approaches by the community. The evaluation identifies a wet bias by Q2Rad in the central plains and the South and then introduces intermediate products to explain it. Finally, this study provides estimates of uncertainty using the radar quality index product for both Q2Rad and the gauge-corrected Q2RadGC daily precipitation products. This error quantification should be useful to the satellite QPE community who use Q2 products as a reference.
Abstract
Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.
Abstract
Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.
Abstract
In this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agency's (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratory's (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering) are applied to the ground radar measurements prior to considering them as reference data. The results show that PR captures well the spatial pattern of total rainfall amounts with a high correlation coefficient (CC; 0.91) with Q2, but this decreases to 0.56 for instantaneous rain rates. In terms of precipitation types, Q2 and PR convective echoes are spatially correlated with a CC of 0.63. Despite this correlation, PR's total annual precipitation from convection is 48.82% less than that by Q2, which points to potential issues in the PR algorithm's attenuation correction, nonuniform beam filling, and/or reflectivity-to-rainfall relation. Finally, the spatial analysis identifies regime-dependent errors, in particular in the mountainous west. It is likely that the surface reference technique is triggered over complex terrain, resulting in high-amplitude biases.
Abstract
In this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agency's (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratory's (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering) are applied to the ground radar measurements prior to considering them as reference data. The results show that PR captures well the spatial pattern of total rainfall amounts with a high correlation coefficient (CC; 0.91) with Q2, but this decreases to 0.56 for instantaneous rain rates. In terms of precipitation types, Q2 and PR convective echoes are spatially correlated with a CC of 0.63. Despite this correlation, PR's total annual precipitation from convection is 48.82% less than that by Q2, which points to potential issues in the PR algorithm's attenuation correction, nonuniform beam filling, and/or reflectivity-to-rainfall relation. Finally, the spatial analysis identifies regime-dependent errors, in particular in the mountainous west. It is likely that the surface reference technique is triggered over complex terrain, resulting in high-amplitude biases.
Abstract
The radar network deployed in southern France during the first special observing period (SOP 1) of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) was designed to precisely document the 3D structure of moist upstream flow impinging on complex terrain as a function of time, height, and along-barrier distance, and surface rainfall patterns associated with orographic precipitation events. This deployment represents one of the most ambitious field experiments yet, endeavoring to collect high-quality observations of thunderstorms and precipitation systems developing over and in the vicinity of a major mountain chain.
Radar observations collected during HyMeX represent a valuable, and potentially unique, dataset that will be used to improve our knowledge of physical processes at play within coastal orographic heavy precipitating systems and to develop, and evaluate, novel radar-based products for research and operational activities. This article provides a concise description of this radar network and discusses innovative research ideas based upon preliminary analyses of radar observations collected during this field project with emphasis on the synergetic use of dual-polarimetric radar measurements collected at multiple frequencies.
Abstract
The radar network deployed in southern France during the first special observing period (SOP 1) of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) was designed to precisely document the 3D structure of moist upstream flow impinging on complex terrain as a function of time, height, and along-barrier distance, and surface rainfall patterns associated with orographic precipitation events. This deployment represents one of the most ambitious field experiments yet, endeavoring to collect high-quality observations of thunderstorms and precipitation systems developing over and in the vicinity of a major mountain chain.
Radar observations collected during HyMeX represent a valuable, and potentially unique, dataset that will be used to improve our knowledge of physical processes at play within coastal orographic heavy precipitating systems and to develop, and evaluate, novel radar-based products for research and operational activities. This article provides a concise description of this radar network and discusses innovative research ideas based upon preliminary analyses of radar observations collected during this field project with emphasis on the synergetic use of dual-polarimetric radar measurements collected at multiple frequencies.
Abstract
Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors, including (i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and (ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.
Abstract
Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors, including (i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and (ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.
