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Abstract
In this paper the influence of surface type, wind speed, and other environmental conditions on near-surface air temperature, specific humidity, and surface temperature is studied. A wireless sensor network consisting of 13 low-cost meteorological stations was set up as a 2.3-km-long double transect in western Germany during the Fluxes and Patterns in the Soil–Vegetation–Atmosphere Scheme (FLUXPAT2009) campaign. This deployment covered various surface types, including a small river. It was found that the air temperature was mainly influenced by the distance to the river and that its variability is controlled by the wind speed. During the night, a pool of cold air formed in the valley close to the water. The specific humidity is also governed by proximity to the river, especially during the night and for low wind speeds. In contrast, the differences in surface temperature were caused by different land cover. These results can be confirmed by a cluster analysis. Setting up 13 stations in a relatively small area is not always feasible. In this study, an estimation of the error that is made by considering the effect of a reduced number of stations is given. Use of only a single station results in an error of 0.86 K in air temperature, 0.67 g kg−1 in specific humidity, and 1.4 K in surface temperature.
Abstract
In this paper the influence of surface type, wind speed, and other environmental conditions on near-surface air temperature, specific humidity, and surface temperature is studied. A wireless sensor network consisting of 13 low-cost meteorological stations was set up as a 2.3-km-long double transect in western Germany during the Fluxes and Patterns in the Soil–Vegetation–Atmosphere Scheme (FLUXPAT2009) campaign. This deployment covered various surface types, including a small river. It was found that the air temperature was mainly influenced by the distance to the river and that its variability is controlled by the wind speed. During the night, a pool of cold air formed in the valley close to the water. The specific humidity is also governed by proximity to the river, especially during the night and for low wind speeds. In contrast, the differences in surface temperature were caused by different land cover. These results can be confirmed by a cluster analysis. Setting up 13 stations in a relatively small area is not always feasible. In this study, an estimation of the error that is made by considering the effect of a reduced number of stations is given. Use of only a single station results in an error of 0.86 K in air temperature, 0.67 g kg−1 in specific humidity, and 1.4 K in surface temperature.
Abstract
Cold pools are mesoscale features that are key for understanding the organization of convection, but are insufficiently captured in conventional observations. This study conducts a statistical characterization of cold-pool passages observed at a 280-m-high boundary layer mast in Hamburg (Germany) and discusses factors controlling their signal strength. During 14 summer seasons 489 cold-pool events are identified from rapid temperature drops below −2 K associated with rainfall. The cold-pool activity exhibits distinct annual and diurnal cycles peaking in July and midafternoon, respectively. The median temperature perturbation is −3.3 K at 2-m height and weakens above. Also the increase in hydrostatic air pressure and specific humidity is largest near the surface. Extrapolation of the vertically weakening pressure signal suggests a characteristic cold-pool depth of about 750 m. Disturbances in the horizontal and vertical wind speed components document a lifting-induced circulation of air masses prior to the approaching cold-pool front. According to a correlation analysis, the near-surface temperature perturbation is more strongly controlled by the pre-event saturation deficit (r = −0.71) than by the event-accumulated rainfall amount (r = −0.35). Simulating the observed temperature drops as idealized wet-bulb processes suggests that evaporative cooling alone explains 64% of the variability in cold-pool strength. This number increases to 92% for cases that are not affected by advection of midtropospheric low-Θ e air masses under convective downdrafts.
Abstract
Cold pools are mesoscale features that are key for understanding the organization of convection, but are insufficiently captured in conventional observations. This study conducts a statistical characterization of cold-pool passages observed at a 280-m-high boundary layer mast in Hamburg (Germany) and discusses factors controlling their signal strength. During 14 summer seasons 489 cold-pool events are identified from rapid temperature drops below −2 K associated with rainfall. The cold-pool activity exhibits distinct annual and diurnal cycles peaking in July and midafternoon, respectively. The median temperature perturbation is −3.3 K at 2-m height and weakens above. Also the increase in hydrostatic air pressure and specific humidity is largest near the surface. Extrapolation of the vertically weakening pressure signal suggests a characteristic cold-pool depth of about 750 m. Disturbances in the horizontal and vertical wind speed components document a lifting-induced circulation of air masses prior to the approaching cold-pool front. According to a correlation analysis, the near-surface temperature perturbation is more strongly controlled by the pre-event saturation deficit (r = −0.71) than by the event-accumulated rainfall amount (r = −0.35). Simulating the observed temperature drops as idealized wet-bulb processes suggests that evaporative cooling alone explains 64% of the variability in cold-pool strength. This number increases to 92% for cases that are not affected by advection of midtropospheric low-Θ e air masses under convective downdrafts.
Abstract
The variability of the raindrop size distribution (DSD) contributes to large parts of the uncertainty in radar-based quantitative rainfall estimates. The variety of microphysical processes acting on the formation of rainfall generally leads to significantly different relationships between radar reflectivity Z and rain rate R for stratiform and convective rainfall. High-resolution observation data from three Micro Rain Radars in northern Germany are analyzed to quantify the potential of dual Z–R relationships to improve radar rainfall estimates under idealized rainfall type identification and separation. Stratiform and convective rainfall are separated with two methods, establishing thresholds for the rain rate-dependent mean drop size and the α coefficient of the power-law Z–R relationship. The two types of dual Z–R relationships are tested against a standard Marshall–Palmer relationship and a globally adjusted single relationship. The comparison of DSD-based and reflectivity-derived rain rates shows that the use of stratiform and convective Z–R relationships reduces the estimation error of the 6-month accumulated rainfall between 30% and 50% relative to a single Z–R relationship. Consistent results for neighboring locations are obtained at different rainfall intensity classes. The range of estimation errors narrows by between 20% and 40% for 10-s-integrated rain rates, dependent on rainfall intensity and separation method. The presented technique also considerably reduces the occurrence of extreme underestimations of the true rain rate for heavy rainfall, which is particularly relevant for operational applications and flooding predictions.
Abstract
The variability of the raindrop size distribution (DSD) contributes to large parts of the uncertainty in radar-based quantitative rainfall estimates. The variety of microphysical processes acting on the formation of rainfall generally leads to significantly different relationships between radar reflectivity Z and rain rate R for stratiform and convective rainfall. High-resolution observation data from three Micro Rain Radars in northern Germany are analyzed to quantify the potential of dual Z–R relationships to improve radar rainfall estimates under idealized rainfall type identification and separation. Stratiform and convective rainfall are separated with two methods, establishing thresholds for the rain rate-dependent mean drop size and the α coefficient of the power-law Z–R relationship. The two types of dual Z–R relationships are tested against a standard Marshall–Palmer relationship and a globally adjusted single relationship. The comparison of DSD-based and reflectivity-derived rain rates shows that the use of stratiform and convective Z–R relationships reduces the estimation error of the 6-month accumulated rainfall between 30% and 50% relative to a single Z–R relationship. Consistent results for neighboring locations are obtained at different rainfall intensity classes. The range of estimation errors narrows by between 20% and 40% for 10-s-integrated rain rates, dependent on rainfall intensity and separation method. The presented technique also considerably reduces the occurrence of extreme underestimations of the true rain rate for heavy rainfall, which is particularly relevant for operational applications and flooding predictions.
Abstract
High-resolution numerical weather prediction (NWP) models produce more detailed precipitation structures but the real benefit is probably the more realistic statistics gained with the higher resolution and not the information on the specific grid point. By evaluating three model pairs, each consisting of a high-resolution NWP system resolving convection explicitly and its low-resolution-driving model with parameterized convection, on different spatial scales and for different thresholds, this paper addresses the question of whether high-resolution models really perform better than their driving lower-resolution counterparts. The model pairs are evaluated by means of two fuzzy verification methods—upscaling (UP) and fractions skill score (FSS)—for the 6 months of the D-PHASE Operations Period and in a highly complex terrain. Observations are provided by the Swiss radar composite and the evaluation is restricted to the area covered by the Swiss radar stations. The high-resolution models outperform or equal the performance of their respective lower-resolution driving models. The differences between the models are significant and robust against small changes in the verification settings. An evaluation based on individual months shows that high-resolution models give better results, particularly with regard to convective, more localized precipitation events.
Abstract
High-resolution numerical weather prediction (NWP) models produce more detailed precipitation structures but the real benefit is probably the more realistic statistics gained with the higher resolution and not the information on the specific grid point. By evaluating three model pairs, each consisting of a high-resolution NWP system resolving convection explicitly and its low-resolution-driving model with parameterized convection, on different spatial scales and for different thresholds, this paper addresses the question of whether high-resolution models really perform better than their driving lower-resolution counterparts. The model pairs are evaluated by means of two fuzzy verification methods—upscaling (UP) and fractions skill score (FSS)—for the 6 months of the D-PHASE Operations Period and in a highly complex terrain. Observations are provided by the Swiss radar composite and the evaluation is restricted to the area covered by the Swiss radar stations. The high-resolution models outperform or equal the performance of their respective lower-resolution driving models. The differences between the models are significant and robust against small changes in the verification settings. An evaluation based on individual months shows that high-resolution models give better results, particularly with regard to convective, more localized precipitation events.
A Simple Method for Attenuation Correction in Local X-Band Radar Measurements Using C-Band Radar DataAbstract
This paper presents a novel, simple method to correct reflectivity measurements of weather radars that operate in attenuation-influenced frequency bands using observations from less attenuated radar systems. In recent years radar systems operating in the X-band frequency range have been developed to provide precipitation fields for areas of special interest in high temporal (≤1 min) and spatial (≤250 m) resolution in complement to nationwide radar networks. However, X-band radars are highly influenced by attenuation. C- and S-band radars typically have coarser resolution (250 m–1 km and 5 min) but are less affected by attenuation.
Correcting for attenuation effects in simple (non-Doppler) single-polarized X-band radars remains challenging and is often dependent on restriction parameters, for example, those derived from mountain returns. Therefore, these algorithms are applicable only in limited areas. The method proposed here uses measurements from C-band radars and hence can be applied in all regions covered by nationwide C- (or S-) band radar networks. First, a single scan of X-band radar measurements is used exemplary to identify advantages and disadvantages of the novel algorithm compared to a standard single radar algorithm. The performance of the correction algorithms in different types of precipitation is examined in nine case studies. The proposed method provides very promising results for each type of precipitation. Additionally, it is evaluated in a 5-month comparison with Micro Rain Radar (MRR) observations. The bias between uncorrected X-band radar and MRR data is nearly eliminated by the attenuation correction algorithm, and the RMSE is reduced by 20% while the correlation of ~0.9 between both systems remains nearly constant.
Abstract
This paper presents a novel, simple method to correct reflectivity measurements of weather radars that operate in attenuation-influenced frequency bands using observations from less attenuated radar systems. In recent years radar systems operating in the X-band frequency range have been developed to provide precipitation fields for areas of special interest in high temporal (≤1 min) and spatial (≤250 m) resolution in complement to nationwide radar networks. However, X-band radars are highly influenced by attenuation. C- and S-band radars typically have coarser resolution (250 m–1 km and 5 min) but are less affected by attenuation.
Correcting for attenuation effects in simple (non-Doppler) single-polarized X-band radars remains challenging and is often dependent on restriction parameters, for example, those derived from mountain returns. Therefore, these algorithms are applicable only in limited areas. The method proposed here uses measurements from C-band radars and hence can be applied in all regions covered by nationwide C- (or S-) band radar networks. First, a single scan of X-band radar measurements is used exemplary to identify advantages and disadvantages of the novel algorithm compared to a standard single radar algorithm. The performance of the correction algorithms in different types of precipitation is examined in nine case studies. The proposed method provides very promising results for each type of precipitation. Additionally, it is evaluated in a 5-month comparison with Micro Rain Radar (MRR) observations. The bias between uncorrected X-band radar and MRR data is nearly eliminated by the attenuation correction algorithm, and the RMSE is reduced by 20% while the correlation of ~0.9 between both systems remains nearly constant.
Abstract
High-quality fields of surface radiation fluxes are required for the development of Land Data Assimilation Systems. A fast offline integration scheme was developed to modify NWP model cloud fields based on Meteosat visible and infrared observations. From the updated cloud fields, downward shortwave and longwave radiation at the surface are computed using the NWP radiative transfer model.
A dataset of 15 months covering Europe was produced and validated against measurements of ground stations on a daily basis. In situ measurements are available for 30 stations in the Netherlands and two Baseline Surface Radiation Network (BSRN) stations in Germany and France. The accuracy of shortwave surface radiation is increased when the integration system is applied. The rms error in the model forecast is found to be 32 and 42 W m−2 for the period from October 1999 to December 2000 for the two BSRN stations. These values are reduced to 21 and 25 W m−2 through the application of the integration scheme. During the summer months the errors are generally larger than in winter. Because of an integrated monitoring of surface albedo, the performance of the scheme is not affected by snow cover. The errors in the longwave radiation field of the original NWP model are already small. However, they are slightly reduced by applying the integration scheme.
Abstract
High-quality fields of surface radiation fluxes are required for the development of Land Data Assimilation Systems. A fast offline integration scheme was developed to modify NWP model cloud fields based on Meteosat visible and infrared observations. From the updated cloud fields, downward shortwave and longwave radiation at the surface are computed using the NWP radiative transfer model.
A dataset of 15 months covering Europe was produced and validated against measurements of ground stations on a daily basis. In situ measurements are available for 30 stations in the Netherlands and two Baseline Surface Radiation Network (BSRN) stations in Germany and France. The accuracy of shortwave surface radiation is increased when the integration system is applied. The rms error in the model forecast is found to be 32 and 42 W m−2 for the period from October 1999 to December 2000 for the two BSRN stations. These values are reduced to 21 and 25 W m−2 through the application of the integration scheme. During the summer months the errors are generally larger than in winter. Because of an integrated monitoring of surface albedo, the performance of the scheme is not affected by snow cover. The errors in the longwave radiation field of the original NWP model are already small. However, they are slightly reduced by applying the integration scheme.
Abstract
Central Europe has a vital and extensive meteorological research community comprising national weather services, universities, and research organizations and institutes. Nearly all of them are involved in the open scientific questions regarding clouds and precipitation processes. The research activities include observations (from in situ ground-based remote sensing radio soundings to satellite-based observations), model development on all scales (from direct numerical simulations to global climate models), and other activities. With Germany as an example our first objective is to show the large amount and the diversity of observations regarding clouds and precipitation. The goal is to give an overview of existing measurements and datasets to show the benefit of combining the different information from a variety of observations. Up to now the access to and the usage of these datasets from different sources was not straightforward, due to the issue of missing data and archiving standards for observational data. This then motivates our second objective, which is to introduce our solution for this issue—the novel Standardized Atmospheric Measurement Data archive (SAMD). SAMD is one of the outcomes of the German research initiative High Definition Clouds and Precipitation for Advancing Climate Prediction [HD(CP)2]. The goal of SAMD is an easy-to-use approach for both data producers and archive users. Therefore the archive provides observational data in the common Climate Forecast (CF) Conventions format and makes it available to the broader public. SAMD offers highly standardized quality-controlled data and metadata for a wide range of instruments, with open access, which makes this novel archive important for the research community.
Abstract
Central Europe has a vital and extensive meteorological research community comprising national weather services, universities, and research organizations and institutes. Nearly all of them are involved in the open scientific questions regarding clouds and precipitation processes. The research activities include observations (from in situ ground-based remote sensing radio soundings to satellite-based observations), model development on all scales (from direct numerical simulations to global climate models), and other activities. With Germany as an example our first objective is to show the large amount and the diversity of observations regarding clouds and precipitation. The goal is to give an overview of existing measurements and datasets to show the benefit of combining the different information from a variety of observations. Up to now the access to and the usage of these datasets from different sources was not straightforward, due to the issue of missing data and archiving standards for observational data. This then motivates our second objective, which is to introduce our solution for this issue—the novel Standardized Atmospheric Measurement Data archive (SAMD). SAMD is one of the outcomes of the German research initiative High Definition Clouds and Precipitation for Advancing Climate Prediction [HD(CP)2]. The goal of SAMD is an easy-to-use approach for both data producers and archive users. Therefore the archive provides observational data in the common Climate Forecast (CF) Conventions format and makes it available to the broader public. SAMD offers highly standardized quality-controlled data and metadata for a wide range of instruments, with open access, which makes this novel archive important for the research community.
Abstract
A configuration of the High-Altitude Long-Range Research Aircraft (HALO) as a remote sensing cloud observatory is described, and its use is illustrated with results from the first and second Next-Generation Aircraft Remote Sensing for Validation (NARVAL) field studies. Measurements from the second NARVAL (NARVAL2) are used to highlight the ability of HALO, when configured in this fashion, to characterize not only the distribution of water condensate in the atmosphere, but also its impact on radiant energy transfer and the covarying large-scale meteorological conditions—including the large-scale velocity field and its vertical component. The NARVAL campaigns with HALO demonstrate the potential of airborne cloud observatories to address long-standing riddles in studies of the coupling between clouds and circulation and are helping to motivate a new generation of field studies.
Abstract
A configuration of the High-Altitude Long-Range Research Aircraft (HALO) as a remote sensing cloud observatory is described, and its use is illustrated with results from the first and second Next-Generation Aircraft Remote Sensing for Validation (NARVAL) field studies. Measurements from the second NARVAL (NARVAL2) are used to highlight the ability of HALO, when configured in this fashion, to characterize not only the distribution of water condensate in the atmosphere, but also its impact on radiant energy transfer and the covarying large-scale meteorological conditions—including the large-scale velocity field and its vertical component. The NARVAL campaigns with HALO demonstrate the potential of airborne cloud observatories to address long-standing riddles in studies of the coupling between clouds and circulation and are helping to motivate a new generation of field studies.
Demonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system.
Demonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system.
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