ORCID Profile
0000-0003-3336-7610
Current Organisation
UNSW Sydney
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 08-2014
Publisher: Copernicus GmbH
Date: 05-09-2017
DOI: 10.5194/AMT-2017-287
Abstract: Abstract. In the last decade there has been a growing interest from the hydrometeorological community regarding rainfall estimation from commercial microwave link (CML) networks. Path-averaged rainfall intensities can be retrieved from the signal attenuation between cell phone towers. Although this technique is still in development, it offers great opportunities for the retrieval of rainfall rates at high spatiotemporal resolutions very close to the Earth's surface. Rainfall measurements at high spatiotemporal resolutions are highly valued in urban hydrology, for instance, given the large impact that flash floods exert on society. Flash floods are triggered by intense rainfall events that develop over short time scales. Here, we present one of the first evaluations of this measurement technique for a subtropical climate. Rainfall estimation for subtropical climates is highly relevant, since many countries with few surface rainfall observations are located in such areas. The test bed of the current study is the Brazilian city of São Paulo. The open-source algorithm RAINLINK was applied to retrieve rainfall intensities from (power) attenuation measurements. The performance of RAINLINK estimates was evaluated for 5 of the 250 CML in the São Paulo metropolitan area for which we received data, for 81 days between October 2014 and January 2015. We evaluated the retrieved rainfall intensities and accumulations from CML against those from a dense automatic gauge network. Results were found to be promising and encouraging, especially for short links, for which high correlations ( 0.9) and low biases (~ 30 % and lower) were obtained.
Publisher: Copernicus GmbH
Date: 27-10-2021
Abstract: Abstract. We present a feasibility study for an object-based method to characterise thunderstorm properties in simulation data from convection-permitting weather models. An existing thunderstorm tracker, the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm, was applied to thunderstorms simulated by the Advanced Research Weather Research and Forecasting (AR-WRF) weather model at convection-permitting resolution for a domain centred on Switzerland. Three WRF microphysics parameterisations were tested. The results are compared to independent radar-based observations of thunderstorms derived using the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm. TRT was specifically designed to track thunderstorms over the complex Alpine topography of Switzerland. The object-based approach produces statistics on the simulated thunderstorms that can be compared to object-based observation data. The results indicate that the simulations underestimated the occurrence of severe and very large hail compared to the observations. Other properties, including the number of storm cells per day, geographical storm hotspots, thunderstorm diurnal cycles, and storm movement directions and velocities, provide a reasonable match to the observations, which shows the feasibility of the technique for characterisation of simulated thunderstorms over complex terrain.
Publisher: Wiley
Date: 24-07-2013
DOI: 10.1002/WSB.315
Publisher: Copernicus GmbH
Date: 08-09-2020
Abstract: Abstract. The lower-order moments of the drop size distribution (DSD) have generally been considered difficult to retrieve accurately from polarimetric radar data because these data are related to higher-order moments. For ex le, the 4.6th moment is associated with a specific differential phase and the 6th moment with reflectivity and ratio of high-order moments with differential reflectivity. Thus, conventionally, the emphasis has been to estimate rain rate (3.67th moment) or parameters of the exponential or gamma distribution for the DSD. Many double-moment “bulk” microphysical schemes predict the total number concentration (the 0th moment of the DSD, or M0) and the mixing ratio (or equivalently, the 3rd moment M3). Thus, it is difficult to compare the model outputs directly with polarimetric radar observations or, given the model outputs, forward model the radar observables. This article describes the use of double-moment normalization of DSDs and the resulting stable intrinsic shape that can be fitted by the generalized gamma (G-G) distribution. The two reference moments are M3 and M6, which are shown to be retrievable using the X-band radar reflectivity, differential reflectivity, and specific attenuation (from the iterative correction of measured reflectivity Zh using the total Φdp constraint, i.e., the iterative ZPHI method). Along with the climatological shape parameters of the G-G fit to the scaled/normalized DSDs, the lower-order moments are then retrieved more accurately than possible hitherto. The importance of measuring the complete DSD from 0.1 mm onwards is emphasized using, in our case, an optical array probe with 50 µm resolution collocated with a two-dimensional video disdrometer with about 170 µm resolution. This avoids small drop truncation and hence the accurate calculation of lower-order moments. A case study of a complex multi-cell storm which traversed an instrumented site near the CSU-CHILL radar is described for which the moments were retrieved from radar and compared with directly computed moments from the complete spectrum measurements using the aforementioned two disdrometers. Our detailed validation analysis of the radar-retrieved moments showed relative bias of the moments M0 through M2 was % in magnitude, with Pearson’s correlation coefficient .9. Both radar measurement and parameterization errors were estimated rigorously. We show that the temporal variation of the radar-retrieved mass-weighted mean diameter with M0 resulted in coherent “time tracks” that can potentially lead to studies of precipitation evolution that have not been possible so far.
Publisher: Wiley
Date: 11-05-2016
DOI: 10.1002/QJ.2801
Publisher: Copernicus GmbH
Date: 20-07-2017
Abstract: Abstract. A new technique for estimating the raindrop size distribution (DSD) from polarimetric radar data is proposed. Two statistical moments of the DSD are estimated from polarimetric variables, and the DSD is reconstructed using a double-moment normalisation. The technique takes advantage of the relative invariance of the double-moment normalised DSD. The method was tested using X-band radar data and networks of disdrometers in three different climatic regions. Radar-derived estimates of the DSD compare reasonably well to observations. In the three tested domains, in terms of DSD moments, rain rate, and characteristic drop diameter, the proposed method performs similarly to and often better than a state-of-the-art DSD-retrieval technique. The approach is flexible because no specific DSD model is prescribed. In addition, a method is proposed to treat noisy radar data to improve DSD-retrieval performance with radar measurements.
Publisher: Copernicus GmbH
Date: 10-10-2023
Publisher: American Meteorological Society
Date: 02-2023
Abstract: We evaluated the performance in Australia of proxies designed to identify atmospheric conditions prone to hail and severe storms. In a convection-resolving but short-duration simulation, proxies that use instability and wind shear thresholds overestimated the probability of hail occurring when compared to the estimated occurrence of surface graupel in the model, particularly in Australia’s tropical north. We used reanalysis data and the Australian Bureau of Meteorology severe storm archive to examine atmospheric conditions at times and locations when hailstorms, other storms, and no storms were reported between January 1979 and March 2021. In instability–shear space, the best discriminator between hail and no-storm times was found to vary predictably with melting-level height, allowing a new proxy to better represent latitudinal trends in atmospheric conditions. We found extra conditions that can be applied to the new proxy to efficiently reduce the number of false alarms. The new proxy outperforms the tested existing proxies for detection of hail-prone conditions in Australia. Hail proxies take a description of the atmosphere, such as its temperature, moisture content, and wind properties at various heights, and determine the likelihood of hail forming and hitting the ground. It is a difficult task prone to uncertainty, but in many locations there are no direct observations of hail, and in these places information from proxies is valuable. Existing proxies have a tendency to overestimate the probability of hail falling in the north of Australia. In this study we developed an updated proxy that uses information about the atmosphere’s melting-level height to refine its hail predictions. The new proxy outperforms other tested proxies for hail in Australia. Accurate hail proxies are important for assessment of past and future changes to hail hazard and risk.
Publisher: American Meteorological Society
Date: 2019
Abstract: Commonly used disdrometers tend not to accurately measure concentrations of very small drops in the raindrop size distribution (DSD), either through truncation of the DSD at the small-drop end or because of large uncertainties on these measurements. Recent studies have shown that, as a result of these inaccuracies, many if not most ground-based disdrometers do not capture the “drizzle mode” of precipitation, which consists of large concentrations of small drops and is often separated from the main part of the DSD by a shoulder region. We present a technique for reconstructing the drizzle mode of the DSD from “incomplete” measurements in which the drizzle mode is not present. Two statistical moments of the DSD that are well measured by standard disdrometers are identified and used with a double-moment normalized DSD function that describes the DSD shape. A model representing the double-moment normalized DSD is trained using measurements of DSD spectra that contain the drizzle mode obtained using collocated Meteorological Particle Spectrometer and 2D video disdrometer instruments. The best-fitting model is shown to depend on temporal resolution. The result is a method to estimate, from truncated or uncertain measurements of the DSD, a more complete DSD that includes the drizzle mode. The technique reduces bias on low-order moments of the DSD that influence important bulk variables such as the total drop concentration and mass-weighted mean drop diameter. The reconstruction is flexible and often produces better rain-rate estimations than a previous DSD correction routine, particularly for light rain.
Publisher: Springer Science and Business Media LLC
Date: 09-02-2021
Publisher: Springer Science and Business Media LLC
Date: 19-09-2023
Publisher: Copernicus GmbH
Date: 26-07-2018
Abstract: Abstract. In the last decade there has been a growing interest from the hydrometeorological community regarding rainfall estimation from commercial microwave link (CML) networks. Path-averaged rainfall intensities can be retrieved from the signal attenuation between cell phone towers. Although this technique is still in development, it offers great opportunities for the retrieval of rainfall rates at high spatiotemporal resolutions very close to the Earth's surface. Rainfall measurements at high spatiotemporal resolutions are highly valued in urban hydrology, for instance, given the large impact that flash floods exert on society. Flash floods are triggered by intense rainfall events that develop over short time scales. Here, we present one of the first evaluations of this measurement technique for a humid subtropical climate. Rainfall estimation for subtropical climates is highly relevant, as many countries with few surface rainfall observations are located in such areas. The test bed of the current study is the Brazilian city of São Paulo. The open-source algorithm RAINLINK was applied to retrieve rainfall intensities from (power) attenuation measurements. The performance of RAINLINK estimates was evaluated for 145 of the 213 CMLs in the São Paulo metropolitan area for which we received data, for 81 days between October 2014 and January 2015. We evaluated the retrieved rainfall intensities and accumulations from CMLs against those from a dense automatic gauge network. Results were found to be promising and encouraging when it comes to capturing the city-average rainfall dynamics. Mixed results were obtained for in idual CML estimates, which may be related to erroneous metadata.
Publisher: American Geophysical Union (AGU)
Date: 12-2018
DOI: 10.1029/2018WR023393
Publisher: American Meteorological Society
Date: 09-2017
Abstract: Universal multifractal (UM) analysis was used to investigate the scaling properties of snowfall at high temporal and spatial resolutions. Snowfall data were recorded using a 2D video disdrometer (2DVD) in the Swiss Alps. Six 1-h-long periods of snowfall, half in calm and half in light wind conditions, were selected for analysis. UM analysis was performed on reconstructed 35-m vertical columns of snowfall structure, snowfall time series at 100-ms resolution, and two-dimensional snowflake accumulation maps over a 5.12 5.12 cm2 area. Multifractal scaling was observed for the vertical structure of snow particle number concentration, for scales between about 35 and 4.4 m, and sometimes down to about 0.5 m. At smaller scales, no scaling was observed. In high-resolution time series of snowfall, evidence of scaling was found for scales between about 7 min and ~26 s in most of the analyzed hours. Snowflake accumulations within a subset of the small s ling area of the 2DVD showed no scaling properties, suggesting homogeneous structure in snowfall at the very small (~5 cm) scale, which agrees with the results for vertical structure and time series.
Publisher: Elsevier BV
Date: 06-2013
Publisher: Wiley
Date: 25-02-2020
DOI: 10.1002/JOC.6514
Publisher: Copernicus GmbH
Date: 22-03-2017
Abstract: Abstract. A comprehensive hydrometeorological dataset is presented spanning the period 1 January 2011–31 December 2014 to improve the understanding of the hydrological processes leading to flash floods and the relation between rainfall, runoff, erosion and sediment transport in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. Badlands are present in the Auzon catchment and well connected to high-gradient channels of bedrock rivers which promotes the transfer of suspended solids downstream. The number of observed variables, the various sensors involved (both in situ and remote) and the space–time resolution ( ∼ km2, ∼ min) of this comprehensive dataset make it a unique contribution to research communities focused on hydrometeorology, surface hydrology and erosion. Given that rainfall is highly variable in space and time in this region, the observation system enables assessment of the hydrological response to rainfall fields. Indeed, (i) rainfall data are provided by rain gauges (both a research network of 21 rain gauges with a 5 min time step and an operational network of 10 rain gauges with a 5 min or 1 h time step), S-band Doppler dual-polarization radars (1 km2, 5 min resolution), disdrometers (16 sensors working at 30 s or 1 min time step) and Micro Rain Radars (5 sensors, 100 m height resolution). Additionally, during the special observation period (SOP-1) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). (ii) Other meteorological data are taken from the operational surface weather observation stations of Météo-France (including 2 m air temperature, atmospheric pressure, 2 m relative humidity, 10 m wind speed and direction, global radiation) at the hourly time resolution (six stations in the region of interest). (iii) The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations estimate water discharge at a 2–10 min time resolution. Two of these stations also measure additional physico-chemical variables (turbidity, temperature, conductivity) and water s les are collected automatically during floods, allowing further geochemical characterization of water and suspended solids. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 sensors installed in the intermittent hydrographic network continuously measures water level and water temperature in headwater subcatchments (from 0.17 to 116 km2) at a time resolution of 2–5 min. A network of soil moisture sensors enables the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, concomitant observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. Finally, this dataset is considered appropriate for understanding the rainfall variability in time and space at fine scales, improving areal rainfall estimations and progressing in distributed hydrological and erosion modelling. DOI of the referenced dataset: doi:10.6096/MISTRALS-HyMeX.1438.
Publisher: Copernicus GmbH
Date: 09-09-2014
Abstract: Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.
Publisher: Copernicus GmbH
Date: 10-10-2023
Publisher: American Meteorological Society
Date: 06-2018
Abstract: Improving the atmospheric component of hydrological models is beneficial for applications such as water resources assessment and hydropower operations. Within this goal, precise characterization of rain microphysics is key for climate and weather modeling, and thus for hydrometeorological applications. Such characterization can be achieved by analyzing the evolution in time of the particle size distribution (PSD) of hydrometeors, which can be measured at ground using disdrometers for validation. The estimation, however, depends on the choice of the PSD form (the shape) and on the parameters to define the exact shape. In the case of modeling rain microphysics, two approaches compete: the use of the number concentration of drops decoupled from the shape of the distribution (the [NT, E(D), E(D2)] and the {NT, E(D), E[log(D)]} models), and the (N0, Λ, μ) model that embeds in N0 both the shape of the distribution and the number concentration of drops. Here we use a comprehensive dataset of disdrometer measurements to show that the NT-based approaches allow a more precise characterization of the drop size distribution (DSD) and also a physically based modeling of the microphysical processes of rain since NT is analytically independent of the shape of the DSD {parameterized by E(D), and E(D2) or E[log(D)]}. The implication is that numerical models would benefit from decoupling the number of drops from the shape of distribution in their modules of precipitation microphysics in order to improve outputs that eventually feed hydrological models.
Publisher: Copernicus GmbH
Date: 17-02-2014
Abstract: Abstract. This paper presents a hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using as input the statistical behavior of a set of particle descriptors, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over precipitation time steps labeled by visual inspection. In this way, 8 dominant hydrometeor classes could be discriminated. The algorithm achieves accurate classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.
Publisher: American Meteorological Society
Date: 06-2017
Abstract: Double-moment normalization of the drop size distribution (DSD) summarizes the DSD in a compact way, using two of its statistical moments and a “generic” double-moment normalized DSD function. Results are presented of an investigation into the invariance of the double-moment normalized DSD through horizontal and vertical displacement in space, using data from disdrometers, vertically pointing K-band Micro Rain Radars, and an X-band polarimetric weather radar. The invariance of the double-moment normalized DSD is tested over a vertical range of up to 1.8 km and a horizontal range of up to approximately 100 km. The results suggest that for practical use, with well-chosen input moments, the double-moment normalized DSD can be assumed invariant in space in stratiform rain. The choice of moments used to characterize the DSD affects the amount of DSD variability captured by the normalization. It is shown that in stratiform rain, it is possible to capture more than 85% of the variability in DSD moments zero to seven using the technique. Most DSD variability in stratiform rain can thus be explained through the variability of two of its statistical moments. The results suggest similar behavior exists in transition and convective rain, but the limited data s les available do not allow for robust conclusions for these rain types. The results have implications for practical uses of double-moment DSD normalization, including the study of DSD variability and microphysics, DSD-retrieval algorithms, and DSD models used in rainfall retrieval.
Publisher: Copernicus GmbH
Date: 23-09-2016
DOI: 10.5194/ESSD-2016-32
Abstract: Abstract. A comprehensive hydrometeorological dataset is presented spanning the period 1 January 2011–31 December 2014 to improve the understanding of the hydrological processes leading to flash floods and the relation between rainfall, runoff, erosion and sediment transport in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. Badlands are present in the Auzon catchment and well connected to high gradient channels of bedrock rivers which promotes the transfer of suspended solids downstream. The specificity of the dataset is its high space-time resolution, especially concerning rainfall and the hydrological response which is particularly adapted to the highly spatially variable rainfall events that may occur in this region. This type of dataset is rare in scientific literature because of the quantity and type of sensors for meteorology and surface hydrology. Rainfall data include continuous precipitation measured by rain gauges (5 min time step for the research network of 21 rain gauges and 5 min or 1 h time step for the operational network of 10 rain gauges), S-band Doppler dual-polarization radars (1 km2, 5 min resolution), disdrometers (16 sensors working at 30 s or 1 min time step) and Micro Rain Radars (5 sensors, 100 m height resolution). Additionally, during the special observation period (SOP-1) and enhanced observation period (Sep–Dec 2012, Sep–Dec 2013) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). Meteorological data are taken from the operational surface weather observation stations of Météo-France (including 2-m air temperature, atmospheric pressure, 2-m relative humidity, 10-m wind speed and direction, global radiation) at the hourly time resolution (6 stations in the region of interest). The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations measure water discharge at a 2 to 10 min time resolution. Two of these stations also measure additional physico-chemical variables (turbidity, temperature, conductivity) and water s les are collected automatically during floods allowing further geochemical characterization of water and suspended solids. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 sensors installed in the intermittent hydrographic network continuously measures water level and water temperature in headwater subcatchments (from 0.17 km2 to 116 km2) at a time resolution of 2–5 min. A network of soil moisture sensors enable the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, opportunistic observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. The data are appropriate for understanding the rainfall variability in time and space at fine scales, improving areal rainfall estimations and progressing in distributed hydrological and erosion modelling. DOI of the referenced dataset: 0.6096/MISTRALS-HyMeX.1438
Publisher: Copernicus GmbH
Date: 16-01-2015
Abstract: Abstract. The raindrop size distribution (DSD) quantifies the microstructure of rainfall and is critical to studying precipitation processes. We present a method to improve the accuracy of DSD measurements from Parsivel (particle size and velocity) disdrometers, using a two-dimensional video disdrometer (2DVD) as a reference instrument. Parsivel disdrometers bin raindrops into velocity and equivolume diameter classes, but may misestimate the number of drops per class. In our correction method, drop velocities are corrected with reference to theoretical models of terminal drop velocity. We define a filter for raw disdrometer measurements to remove particles that are unlikely to be plausible raindrops. Drop concentrations are corrected such that on average the Parsivel concentrations match those recorded by a 2DVD. The correction can be trained on and applied to data from both generations of OTT Parsivel disdrometers, and indeed any disdrometer in general. The method was applied to data collected during field c aigns in Mediterranean France for a network of first- and second-generation Parsivel disdrometers, and on a first-generation Parsivel in Payerne, Switzerland. We compared the moments of the resulting DSDs to those of a collocated 2DVD, and the resulting DSD-derived rain rates to collocated rain gauges. The correction improved the accuracy of the moments of the Parsivel DSDs, and in the majority of cases the rain rate match with collocated rain gauges was improved. In addition, the correction was shown to be similar for two different climatologies, suggesting its general applicability.
Publisher: Copernicus GmbH
Date: 28-05-2021
DOI: 10.5194/GMD-2021-105
Abstract: Abstract. We present a feasibility study for an object-based method to characterise thunderstorm properties in simulation data from convection-permitting weather models. An existing thunderstorm tracker, the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm, was applied to thunderstorms simulated by the Advanced Research Weather Research and Forecasting (AR-WRF) weather model at convection-permitting resolution for a domain centred on Switzerland. Three WRF microphysics parameterisations were tested. The results are compared to independent radar-based observations of thunderstorms derived using the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm. TRT was specifically designed to track thunderstorms over the complex Alpine topography of Switzerland. The object-based approach produces statistics on the simulated thunderstorms that can be compared to object-based observation data. The results indicate that the simulations underestimated the occurrence of severe and very large hail compared to the observations. Other properties, including the number of storm cells per day, geographical storm hotspots, thunderstorm diurnal cycles, and storm movement directions and velocities, provide a reasonable match to the observations, which shows the feasibility of the technique for characterisation of simulated thunderstorms over complex terrain.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-15911
Abstract: & & Several remarkable hailstorms have occurred on the territory of Switzerland during the month& br& of May, 2018.& br& This period has been simulated, using the WRF4.0 model at a convection-permitting& br& resolution (1.5 km), using different microphysical schemes (Thompson, Morrison, P3).& br& The surrogate climate change approach has been used for imitating the climate conditions,& br& corresponding to the end of the 21st century (CMIP5 model data, RCP8.5 scenario).& br& The HAILCAST-1D model output has been used as a measure of simulated hail size and 5-& br& minute 3-D radar reflectivity field has been used for cell identification and tracking.& br& Hailstorms produced in the current climate and in surrogate climate change simulations have& br& been examined using neighborhood methods and a storm-tracking algorithm. Current-climate& br& simulated hailstorms were compared with the ground observations and MeteoSwiss radar& br& data.& br& The influence of microphysical schemes to the characteristics of simulated hailstorms has& br& been studied.& & &
Publisher: American Meteorological Society
Date: 07-2016
Abstract: The drop size distribution (DSD) describes the microstructure of liquid precipitation. The high variability of the DSD reflects the variety of microphysical processes controlling raindrop properties and affects the retrieval of rainfall. An analysis of the effects of DSD subgrid variability on areal estimation of precipitation is presented. Data used were recorded with a network of disdrometers in Ardèche, France. DSD variability was studied over two typical scales: 5 km × 5 km, similar to the ground footprint size of the Global Precipitation Measurement (GPM) spaceborne weather radar, and 2.8 km × 2.8 km, an operational pixel size of the Consortium for Small-Scale Modeling (COSMO) numerical weather model. Stochastic simulation was used to generate high-resolution grids of DSD estimates over the regions of interest, constrained by experimental DSDs measured by disdrometers. From these grids, areal DSD estimates were derived. The error introduced by assuming a point measurement to be representative of the areal DSD was quantitatively characterized and was shown to increase with the size of the considered area and with drop size and to decrease with the integration time. The controlled framework allowed for the accuracy of retrieval algorithms to be investigated. Rainfall variables derived by idealized simulations of GPM- and COSMO-style algorithms were compared to subgrid distributions of the same variables. While rain rate and radar reflectivity were well represented, the estimated drop concentration and mass-weighted mean drop diameter were often less representative of subgrid values.
No related grants have been discovered for Timothy Raupach.