Search Results
You are looking at 1 - 10 of 20 items for
- Author or Editor: Robert J. Kuligowski x
- Refine by Access: All Content x
Abstract
Estimates of precipitation from satellite data can provide timely information about rainfall in regions for which data from rain gauge networks are sparse or unavailable entirely and for which radar data are unavailable or are compromised by range effects and beam blockage. Two basic kinds of satellite-based estimates are available. Infrared data from geostationary satellite platforms such as the Geostationary Operational Environmental Satellite (GOES) can be used to infer cloud-top conditions on a continuous basis, but the relationship between cloud-top conditions and the rate of rainfall below can vary significantly. Microwave radiances are related more directly to precipitation rates, but microwave instruments are limited to polar-orbiting platforms, resulting in intermittent availability of estimates. A number of authors have made efforts to combine the strengths of both by using the microwave-based estimates to adjust the GOES-based estimates, mainly for long-term precipitation estimates at coarse spatial resolution. The self-calibrating multivariate precipitation retrieval (SCaMPR) technique represents an approach for doing the same for fine timescales and short time periods. This algorithm first selects an optimal predictor for separating raining from nonraining pixels, calibrates it to raining and nonraining areas from a Special Sensor Microwave Imager (SSM/I) algorithm, and then selects an optimal rain-rate predictor and calibrates it to the SSM/I rain rate for the raining pixels via linear regression. The performance of SCaMPR compared favorably with the autoestimator (AE) technique and GOES multispectral rainfall algorithm (GMSRA) when compared with rain gauge data for three cases. The linear correlations between the estimates and rain gauge observations were similar, but SCaMPR exhibited significantly less bias than did AE and GMSRA.
Abstract
Estimates of precipitation from satellite data can provide timely information about rainfall in regions for which data from rain gauge networks are sparse or unavailable entirely and for which radar data are unavailable or are compromised by range effects and beam blockage. Two basic kinds of satellite-based estimates are available. Infrared data from geostationary satellite platforms such as the Geostationary Operational Environmental Satellite (GOES) can be used to infer cloud-top conditions on a continuous basis, but the relationship between cloud-top conditions and the rate of rainfall below can vary significantly. Microwave radiances are related more directly to precipitation rates, but microwave instruments are limited to polar-orbiting platforms, resulting in intermittent availability of estimates. A number of authors have made efforts to combine the strengths of both by using the microwave-based estimates to adjust the GOES-based estimates, mainly for long-term precipitation estimates at coarse spatial resolution. The self-calibrating multivariate precipitation retrieval (SCaMPR) technique represents an approach for doing the same for fine timescales and short time periods. This algorithm first selects an optimal predictor for separating raining from nonraining pixels, calibrates it to raining and nonraining areas from a Special Sensor Microwave Imager (SSM/I) algorithm, and then selects an optimal rain-rate predictor and calibrates it to the SSM/I rain rate for the raining pixels via linear regression. The performance of SCaMPR compared favorably with the autoestimator (AE) technique and GOES multispectral rainfall algorithm (GMSRA) when compared with rain gauge data for three cases. The linear correlations between the estimates and rain gauge observations were similar, but SCaMPR exhibited significantly less bias than did AE and GMSRA.
Abstract
Accurate, timely, site-specific forecasts of precipitation are important for accurately predicting streamflow and flash floods in small drainage basins. However, presently available numerical weather prediction models do not generally provide forecasts with the accuracy and/or resolution appropriate for this task. A wide variety of approaches to small-scale, short-term precipitation forecasting have been investigated by numerous authors; this paper describes a simple precipitation forecasting model based on artificial neural networks. The model uses the radiosonde-based 700-hPa wind direction and antecedent precipitation data from a rain gauge network to generate short-term (0–6 h) precipitation forecasts for a target location. The performance of the model is illustrated for a gauge in eastern Pennsylvania.
Abstract
Accurate, timely, site-specific forecasts of precipitation are important for accurately predicting streamflow and flash floods in small drainage basins. However, presently available numerical weather prediction models do not generally provide forecasts with the accuracy and/or resolution appropriate for this task. A wide variety of approaches to small-scale, short-term precipitation forecasting have been investigated by numerous authors; this paper describes a simple precipitation forecasting model based on artificial neural networks. The model uses the radiosonde-based 700-hPa wind direction and antecedent precipitation data from a rain gauge network to generate short-term (0–6 h) precipitation forecasts for a target location. The performance of the model is illustrated for a gauge in eastern Pennsylvania.
Abstract
The evolution of precipitation features during a severe wintertime rainfall and flooding event associated with a cold front that crossed the central Appalachians on 19 January 1996 is illustrated through the analysis of radiosonde, rainfall, and streamflow gauge data, and WSR-88D images. Striking evidence of the linkage between heavy precipitation cells and orography was obtained by tracking the movement of the center of mass of storm precipitation, which closely followed the contours of regional orographic features. Higher intensity precipitation cells were consistently located windward of the orographic crest, and the trajectory described by the center of mass of precipitation was also consistent with the spatial arrangement of the river basins where hazardous flooding occurred. Persistent, low-intensity (⩽5 mm h−1) rainfall was registered in these basins during the 12-h period that preceded the arrival of frontal storm activity. It is argued that this prefrontal precipitation had a critical impact on watershed rainfall-runoff response and snowpack conditioning during and after the passage of the front. The intent here is to investigate the links between the observed space–time variability of rainfall and the influence of terrain features on mesoscale circulations in the lee side of the Appalachians. In particular, the viability of orographic mechanisms such as forced ascent, lee-wave interference, and precipitation scavenging of shallow orographic clouds was assessed using simple models and the available meteorological and hydrological data.
Abstract
The evolution of precipitation features during a severe wintertime rainfall and flooding event associated with a cold front that crossed the central Appalachians on 19 January 1996 is illustrated through the analysis of radiosonde, rainfall, and streamflow gauge data, and WSR-88D images. Striking evidence of the linkage between heavy precipitation cells and orography was obtained by tracking the movement of the center of mass of storm precipitation, which closely followed the contours of regional orographic features. Higher intensity precipitation cells were consistently located windward of the orographic crest, and the trajectory described by the center of mass of precipitation was also consistent with the spatial arrangement of the river basins where hazardous flooding occurred. Persistent, low-intensity (⩽5 mm h−1) rainfall was registered in these basins during the 12-h period that preceded the arrival of frontal storm activity. It is argued that this prefrontal precipitation had a critical impact on watershed rainfall-runoff response and snowpack conditioning during and after the passage of the front. The intent here is to investigate the links between the observed space–time variability of rainfall and the influence of terrain features on mesoscale circulations in the lee side of the Appalachians. In particular, the viability of orographic mechanisms such as forced ascent, lee-wave interference, and precipitation scavenging of shallow orographic clouds was assessed using simple models and the available meteorological and hydrological data.
Abstract
Radiance measurements from satellites offer the opportunity to retrieve atmospheric variables at much higher spatial resolution than is presently afforded by in situ measurements (e.g., radiosondes). However, the accuracy of these retrievals is crucial to their usefulness, and the ill-posed nature of the problem precludes a straightforward solution. A number of retrieval approaches have been investigated, including empirical techniques, coupling with numerical weather prediction models, and data analysis techniques such as regression. In this paper, artificial neural networks are used to retrieve vertical temperature and dewpoint profiles from infrared and microwave brightness temperatures from a polar-orbiting satellite. This approach allows retrievals to be performed even in cloudy conditions—a limitation of infrared-only retrievals. In a direct comparison of this technique with results from the operational Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) retrievals, it was found that the neural-network temperature retrievals had larger errors than the ATOVS retrievals (though generally smaller than the first guess used in the ATOVS retrievals) but that the dewpoint retrievals showed consistent improvement over the comparable ATOVS retrievals.
Abstract
Radiance measurements from satellites offer the opportunity to retrieve atmospheric variables at much higher spatial resolution than is presently afforded by in situ measurements (e.g., radiosondes). However, the accuracy of these retrievals is crucial to their usefulness, and the ill-posed nature of the problem precludes a straightforward solution. A number of retrieval approaches have been investigated, including empirical techniques, coupling with numerical weather prediction models, and data analysis techniques such as regression. In this paper, artificial neural networks are used to retrieve vertical temperature and dewpoint profiles from infrared and microwave brightness temperatures from a polar-orbiting satellite. This approach allows retrievals to be performed even in cloudy conditions—a limitation of infrared-only retrievals. In a direct comparison of this technique with results from the operational Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) retrievals, it was found that the neural-network temperature retrievals had larger errors than the ATOVS retrievals (though generally smaller than the first guess used in the ATOVS retrievals) but that the dewpoint retrievals showed consistent improvement over the comparable ATOVS retrievals.
Abstract
Although the resolution of numerical weather prediction models continues to improve, many of the processes that influence precipitation are still not captured adequately by the scales of present operational models, and consequently precipitation forecasts have not yet reached the level of accuracy needed for hydrologic forecasting. Postprocessing of model output to account for local differences can enhance the accuracy and usefulness of these forecasts. Model Output Statistics have performed this important function for a number of years via regression techniques; this paper presents an alternate approach that uses artificial neural networks to produce 6-h precipitation forecasts for specific locations. Tests performed on four locations in the middle Atlantic region of the United States show that the accuracy of the forecasts produced using neural networks compares favorably with those generated using linear regression, especially for heavier precipitation amounts.
Abstract
Although the resolution of numerical weather prediction models continues to improve, many of the processes that influence precipitation are still not captured adequately by the scales of present operational models, and consequently precipitation forecasts have not yet reached the level of accuracy needed for hydrologic forecasting. Postprocessing of model output to account for local differences can enhance the accuracy and usefulness of these forecasts. Model Output Statistics have performed this important function for a number of years via regression techniques; this paper presents an alternate approach that uses artificial neural networks to produce 6-h precipitation forecasts for specific locations. Tests performed on four locations in the middle Atlantic region of the United States show that the accuracy of the forecasts produced using neural networks compares favorably with those generated using linear regression, especially for heavier precipitation amounts.
Abstract
Flash floods are among the most devastating natural weather hazards in the United States, causing an average of more than 225 deaths and $4 billion in property damage annually. As a result, prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. Data from geostationary and polar-orbiting satellites are significant sources of information for the diagnosis and prediction of heavy precipitation and flash floods. Geostationary satellites are especially important for their unique ability simultaneously to observe the atmosphere and its cloud cover from the global scale down to the storm scale at high resolution in both time (every 15 min) and space (1–4 km). This capability makes geostationary satellite data ideally suited for estimating and predicting heavy precipitation, especially during flash-flood events. Presented in this paper are current and future efforts in the National Environmental Satellite, Data, and Information Service that support National Weather Service River Forecast Centers and Weather Forecast Offices during extreme-precipitation events.
Abstract
Flash floods are among the most devastating natural weather hazards in the United States, causing an average of more than 225 deaths and $4 billion in property damage annually. As a result, prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. Data from geostationary and polar-orbiting satellites are significant sources of information for the diagnosis and prediction of heavy precipitation and flash floods. Geostationary satellites are especially important for their unique ability simultaneously to observe the atmosphere and its cloud cover from the global scale down to the storm scale at high resolution in both time (every 15 min) and space (1–4 km). This capability makes geostationary satellite data ideally suited for estimating and predicting heavy precipitation, especially during flash-flood events. Presented in this paper are current and future efforts in the National Environmental Satellite, Data, and Information Service that support National Weather Service River Forecast Centers and Weather Forecast Offices during extreme-precipitation events.
Abstract
The use of multisensor, multifrequency satellite data to specify initial conditions for numerical weather prediction (NWP) models offers a unique opportunity to improve the depiction of small-scale processes in the atmosphere through a myriad of data assimilation approaches. The authors previously developed an algorithm to retrieve temperature and dewpoint profiles from a combination of infrared [high-resolution infrared radiation sounder (HIRS), 18–20-km resolution] and microwave [Advanced Microwave Sounding Unit-A (AMSU-A), 48-km resolution] data, using collocated radiosondes. Besides (and separately from) the estimation problem, one key question in the context of model initialization is how to blend multiresolution data to generate fields at the spatial resolution of the NWP model of interest. In this paper, a fractal downscaling technique is proposed to blend multiresolution satellite data and generate brightness temperature fields at 1-km resolution. The downscaled HIRS and AMSU-A data subsequently can be processed by the retrieval algorithm to derive temperature and dewpoint fields at the same resolution. The utility of these products as an initial condition for NWP models was assessed in the context of regional quantitative precipitation forecasting (QPF) applications using a limited-area orographic precipitation model nested with a mesoscale model. Results from the simulation of a wintertime storm in the Pocono Mountains of the mid-Atlantic region show improvement in QPF skill when the satellite-derived initial conditions were used. However, the disparity between the sparse times when the satellite data are available (12-h intervals) vis-a-vis the hourly import of boundary conditions from the host model lessens the impact of improved initial conditions. This result suggests that gains in QPF skill are linked to the availability of relevant remote sensing data at time intervals consistent with the useful memory of initial conditions in NWP models.
Abstract
The use of multisensor, multifrequency satellite data to specify initial conditions for numerical weather prediction (NWP) models offers a unique opportunity to improve the depiction of small-scale processes in the atmosphere through a myriad of data assimilation approaches. The authors previously developed an algorithm to retrieve temperature and dewpoint profiles from a combination of infrared [high-resolution infrared radiation sounder (HIRS), 18–20-km resolution] and microwave [Advanced Microwave Sounding Unit-A (AMSU-A), 48-km resolution] data, using collocated radiosondes. Besides (and separately from) the estimation problem, one key question in the context of model initialization is how to blend multiresolution data to generate fields at the spatial resolution of the NWP model of interest. In this paper, a fractal downscaling technique is proposed to blend multiresolution satellite data and generate brightness temperature fields at 1-km resolution. The downscaled HIRS and AMSU-A data subsequently can be processed by the retrieval algorithm to derive temperature and dewpoint fields at the same resolution. The utility of these products as an initial condition for NWP models was assessed in the context of regional quantitative precipitation forecasting (QPF) applications using a limited-area orographic precipitation model nested with a mesoscale model. Results from the simulation of a wintertime storm in the Pocono Mountains of the mid-Atlantic region show improvement in QPF skill when the satellite-derived initial conditions were used. However, the disparity between the sparse times when the satellite data are available (12-h intervals) vis-a-vis the hourly import of boundary conditions from the host model lessens the impact of improved initial conditions. This result suggests that gains in QPF skill are linked to the availability of relevant remote sensing data at time intervals consistent with the useful memory of initial conditions in NWP models.
Abstract
Data from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.
Abstract
Data from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.
Abstract
To date, little objective verification has been performed for rainfall predictions from numerical forecasts of landfalling tropical cyclones. Until 2001, digital output from the operational version of the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane forecast model was available only on a 1° grid. The GFDL model was rerun or reanalyzed for 25 U.S. landfalling tropical cyclones from 1995 to 2002 to obtain higher resolution (1/3°) output. Several measures of forecast quality were used to evaluate the predicted rainfall from these runs, using daily rain gauge data as ground truth. The overall quality was measured by the mean error and bias averaged over all the gauge sites. An estimate of the quality of the forecasted pattern was obtained through the correlation coefficient of the model versus gauge values. In addition, more traditional precipitation verification scores were calculated including equitable threat and bias scores. To evaluate the skill of the rainfall forecasts, a simple rainfall climatology and persistence (R-CLIPER) model was developed, where a climatological rainfall rate is accumulated along either the forecasted or observed storm track. Results show that the R-CLIPER and GFDL forecasts had comparable mean absolute errors of ∼0.9 in. (23 mm) for the 25 cases. The GFDL model exhibited a higher pattern correlation with observations than R-CLIPER, but still only explained ∼30% of the spatial variance. The GFDL model also had higher equitable threat scores than R-CLIPER, partially because of a low bias of R-CLIPER for rainfall amounts larger than 0.5 in. (13 mm). A large case-to-case variability was found that was dependent on both synoptic conditions and track error.
Abstract
To date, little objective verification has been performed for rainfall predictions from numerical forecasts of landfalling tropical cyclones. Until 2001, digital output from the operational version of the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane forecast model was available only on a 1° grid. The GFDL model was rerun or reanalyzed for 25 U.S. landfalling tropical cyclones from 1995 to 2002 to obtain higher resolution (1/3°) output. Several measures of forecast quality were used to evaluate the predicted rainfall from these runs, using daily rain gauge data as ground truth. The overall quality was measured by the mean error and bias averaged over all the gauge sites. An estimate of the quality of the forecasted pattern was obtained through the correlation coefficient of the model versus gauge values. In addition, more traditional precipitation verification scores were calculated including equitable threat and bias scores. To evaluate the skill of the rainfall forecasts, a simple rainfall climatology and persistence (R-CLIPER) model was developed, where a climatological rainfall rate is accumulated along either the forecasted or observed storm track. Results show that the R-CLIPER and GFDL forecasts had comparable mean absolute errors of ∼0.9 in. (23 mm) for the 25 cases. The GFDL model exhibited a higher pattern correlation with observations than R-CLIPER, but still only explained ∼30% of the spatial variance. The GFDL model also had higher equitable threat scores than R-CLIPER, partially because of a low bias of R-CLIPER for rainfall amounts larger than 0.5 in. (13 mm). A large case-to-case variability was found that was dependent on both synoptic conditions and track error.
Abstract
Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs). As DCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithm biases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicating Q2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3–4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.
Abstract
Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs). As DCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithm biases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicating Q2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3–4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.
