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Abstract In this paired study, we apply a recently developed high-resolution bathymetry estimation algorithm ("cBathy") to X-band marine radar observations at two nearshore field sites. The algorithm exploits observations of the spatial structure of wave phase to attain wavenumber estimates, inverts the linear water wave dispersion relation for depth, and then applies a Kalman filter to objectively update the bathymetry estimates. Previously, performance has only been tested using optical video observations. In this first of two papers, performance of the algorithm using X-band radar image time series is tested at two disparate barred beach environments: Duck, NC, USA, and Benson Beach, WA, USA. Each of the test beaches is either co-located with (Duck, NC) or geographically close to (Benson Beach, WA) those utilized in the original algorithm verification. Concurrent echosounder surveys are used as ground truth. The bulk performance of the radar-derived bathymetry estimate at Duck, NC, achieves 0.49 m root-mean-square error (RMSE) with 0.26 m bias deep. This compares well with the bulk performance of the concurrent estimate derived using optical video (0.44 m RMSE and 0.23 m bias deep). The radar-derived bathymetry estimate performance at Benson Beach, is similar (0.35 m RMSE and 0.11 m bias shallow), and is comparable to that of an optical video derived estimate at a similar Pacific Northwest beach (0.56 m RMSE and 0.41 m bias shallow). At both beaches, significantly higher performance is achieved at locations deeper than 2 m (offshore of the surfzone) than at locations shallower than 2 m (surfzone), where errors are often a large fraction of the total depth. Lastly, several weeks of observations are utilized to assess the sensitivity of algorithm quality control to environmental conditions. Thresholds based on the shoreward component of wind stress and offshore wave steepness are identified and shown to impact the areal coverage of radar-derived bathymetric estimates. Overall, these results demonstrate the viability of marine radar observations as input to the cBathy algorithm and delineate some environmental constraints on algorithm performance. In the companion paper, the algorithm is extended to areas where tidal currents are important, including an ebb tidal shoal and an estuary mouth. Highlights • Depth estimation algorithm, cBathy, is applied to X-band radar image time series. • Performance is assessed at two beaches (North Carolina and Washington, USA). • Accuracy via radar is comparable to video-based performance (reported previously). • Frequency of updates increases with wave height and when wind blows toward shore. [ABSTRACT FROM AUTHOR]

• Precipitation annual maxima estimated from NEXRAD are evaluated over the CONUS. • Accuracy of radar-based estimates varies with magnitude and duration. • Uncertainty in radar-based IDF curves exhibits climate dependence. The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002–2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes. [ABSTRACT FROM AUTHOR]

Highlights • Evaluates utility of scatterometer data for estimation of urban built-up volume. • Dense Sampling Method-processed scatterometer data are compared to airborne lidar. • Analysis conducted for nine urban areas in the United States. • Strong, linear correlations observed between radar- and lidar-derived datasets. • Results promote three-dimensional approach to land use/land cover analyses. Abstract Accurately mapping urban infrastructure and extent is a high priority for resource management and service allocation as well as for addressing environmental, socioeconomic, and geopolitical concerns. Most available data products only document surficial (two-dimensional) land use and land cover (LULC), yet a substantial component of urban growth occurs in the vertical dimension. Light detection and ranging (lidar) data offer the potential for monitoring three-dimensional (3D) change, but the extreme lack of systematic lidar coverage worldwide inflicts considerable gaps in both spatial and temporal coverage. Satellite scatterometer (radar) data may serve as an alternative data source for characterizing urban growth and development in both the horizontal and vertical directions. The accuracy of these radar-based datasets for estimating building volumes remains to be validated quantitatively. For nine U.S. cities, we test whether scatterometer data can be used to estimate 3D urban built-up volume. We found strong, linear correlations between the lidar-derived and radar-derived building volume estimates for all cities with r2 values as high as 0.98 when using spatial trend analysis. Given the high expense that limits lidar data acquisition to small areas at sporadic points in time, satellite scatterometer data provide a breakthrough method for monitoring both vertical growth and horizontal expansion of cities across the world with a continuous decadal time scale. [ABSTRACT FROM AUTHOR]

We analyzed 4-years of Advanced Scatterometer (ASCAT) incidence angle normalized C-band backscatter, Next-Generation Radar (NEXRAD) precipitation and United States Department of Agriculture (USDA) crop yield data over the contiguous United States (U.S.). Large negative anomalies in backscatter are evident over the U.S. during the 2011 and 2012 growing seasons, coincident with two instances of severe drought. The backscatter anomalies were correlated with diminished growing season precipitation and with reduced crop yields reflecting where drought-induced impacts on above-ground biomass production occurred. During periods of acute drought, differences in diurnal backscatter were reduced and in some cases reversed reflecting diminished nocturnal leave rehydration. The results indicate that the C-band, VV-polarized ASCAT backscatter data is sensitive to interannual variability in agricultural productivity rather than soil moisture, and can offer an important geophysical monitoring tool capable of identifying and assessing drought-related vegetation responses independent of and complementary to satellite optical/near-infrared remote sensing data. This technique is flexible and adaptable, providing a capability to address region-specific needs. The twice-daily, weather independent and near-real time global monitoring capabilities of ASCAT and similar microwave instruments (e.g. RapidSCAT, Sentinel-1) can help improve drought preparedness and mitigation planning allowing improved water resource management for agriculture on the local as well as the state level. [ABSTRACT FROM AUTHOR]

Abstract: Ground-based interferometric radar (GBIR) monitoring was conducted on a slow-moving, translational failure landslide in Granby, Grand County, Colorado, USA. Radar monitoring was completed over two separate surveys in 2011 using a tripod mounted real aperture sensor. The purpose of this work is to evaluate GBIR as a temporally dense monitoring technique for monitoring landslide displacement and compare the monitoring results to ongoing GPS based surveying methods to verify measured displacements. We discuss the strengths and limitations of GBIR displacement monitoring with a variety of available sensors, and place this monitoring platform, sensor, and workflow into context of previous slope stability monitoring with GBIR. For both surveys, displacement time series were created through a small temporal baseline stacking to reduce noise and maintain high temporal resolution. The results of the displacement time series were compared to average displacement rates derived from GPS based surveying. An overall verification of radar and GPS derived displacement rates was achieved, and identifies important differences relating to the precision and uncertainty of the two techniques. This work demonstrates GBIR monitoring capability of establishing high temporal resolution on tracking variable rates of landslide movements. Spatial modeling of total observed displacements was completed for both surveys verifying a conceptual model of uniform translational landslide movement, providing greater confidence for mitigation planning. [Copyright &y& Elsevier]

Abstract: A substantial number of wind energy projects have been stalled or abandoned in the United States of America (US) due to concerns over the effects of wind turbines on radar installations. Between 2008 and 2010, military, airspace, or meteorological radar concerns in the US contributed to the delay or abandonment of an estimated 20,000 MW of wind energy capacity. These delays are a likely major factor influencing the current US Administration''s failure to double non-hydro renewable generation from 2008 to 2011; a target stated by the US President in a joint session address to Congress in February 2009. The delays are also a threat to the US Department of Energy''s target to produce 20% of electricity from wind energy by 2030 – unless radar-related barriers are mitigated. This work includes interviews with two senior representatives, from the US Department of Defence (DOD) and the American Wind Energy Association (AWEA), discussing the nature of concerns pertaining to the effects of wind turbines on radar and military/aviation in the US alongside approaches that have been trialled that aim to resolve such concerns. This research finds that the Energy Siting Clearing House, established within the DOD to review delayed wind farm projects, has brought much needed coordination to the approval process. A key challenge for any review body, however, will be to deliver an objective outcome that is not overturned by alternative political agendas. Integral to the success of any approach will be a sufficient capacity and mandate to facilitate the technical and non-technical cross-disciplinary and interagency research generating a balance between military, airspace, meteorological, and wind energy industry/political objectives. [Copyright &y& Elsevier]

• Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. • Noncontact radar-based discharge estimates can be as accurate as in-situ, conventional streamgauges. • There was a notable phase sequencing between the surface velocity and stage, indicative of unsteady flows. • Real-time alerting capabilities of an impending flash flood are demonstrated in a vulnerable, ungauged basin. Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams. [ABSTRACT FROM AUTHOR]

Abstract: The hazards attributed to supercell thunderstorms are primarily considered to be tornadoes, large hail, and damaging winds, but flooding from heavy and extreme rainfall is not as often considered with these storms. As a result there has been little research on the role that supercells play in the production of heavy and extreme rainfall events. In order to assess the contribution of supercells to the climatology of short-duration precipitation extremes the present study uses the Warning Decision Support System — Integrated Information to objectively identify supercell thunderstorms from mosaicked radar data during the year 2009 and associate them with high-resolution, accurate multisensor precipitation estimates at time scales of one hour. Supercells are found to be more likely than non-supercells to produce extreme and heavy precipitation, and comparisons are also made between storm types according to month. [Copyright &y& Elsevier]

Abstract: Radar instruments are part of the core payload of the two Europa Jupiter System Mission (EJSM) spacecraft: NASA-led Jupiter Europa Orbiter (JEO) and ESA-led Jupiter Ganymede Orbiter (JGO). At this point of the project, several frequency bands are under study for radar, which ranges between 5 and 50MHz. Part of this frequency range overlaps with that of the natural jovian radio emissions, which are very intense in the decametric range, below 40MHz. Radio observations above 40MHz are free of interferences, whereas below this threshold, careful observation strategies have to be investigated. We present a review of spectral intensity, variability and sources of these radio emissions. As the radio emissions are strongly beamed, it is possible to model the visibility of the radio emissions, as seen from the vicinity of Europa or Ganymede. We have investigated Io-related radio emissions as well as radio emissions related to the auroral oval. We also review the radiation belts synchrotron emission characteristics. We present radio sources visibility products (dynamic spectra and radio source location maps, on still frames or movies), which can be used for operation planning. This study clearly shows that a deep understanding of the natural radio emissions at Jupiter is necessary to prepare the future EJSM radar instrumentation. We show that this radio noise has to be taken into account very early in the observation planning and strategies for both JGO and JEO. We also point out possible synergies with RPW (Radio and Plasma Waves) instrumentations. [Copyright &y& Elsevier]

Abstract: Horizontal wind data over Indonesia obtained by global reanalyses were compared with wind-profiling radar (WPR) observations in Indonesia at Kototabang (100.32°E, 0.20°S), Pontianak (109.37°E, 0.00°S), Manado (124.92°E, 1.55°N), and Biak (136.10°E, 1.18°S). The global-reanalysis data were 850- and 700-hPa horizontal winds for 2007 and 2008. These data were obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis (R1), the NCEP-Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R2), the Japan Meteorological Agency Climate Data Assimilation System (JCDAS), the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis-interim (ERA-Interim). All the four reanalyses showed good agreement with the WPR zonal and meridional wind data (correlation coefficient >0.7 in most cases). Of the four reanalyses, ERA-Interim had the highest correlation coefficient with the WPR winds in both zonal (correlation coefficient 0.89) and meridional (0.77) components. It can be explained that ERA-Interim used four-dimensional variational analysis (4D-var) for data assimilation, whereas the other reanalyses used three-dimensional variational analysis (3D-var). The zonal winds of the four reanalyses had smaller (78–87%) amplitudes than did the WPR winds. Further comparison of the zonal winds between the global reanalyses and operational radiosondes indicated that global-reanalysis zonal winds with smaller amplitudes are common in and around Indonesia. [Copyright &y& Elsevier]

Abstract: Precipitation is one important input variable for land surface hydrologic and ecological models. Next Generation Radar (NEXRAD) can provide precipitation products that cover most of the conterminous United States at high resolution (approximately 4 km × 4 km). There are two major issues concerning the application of NEXRAD data: 1) the lack of a NEXRAD geo-processing and geo-referring program and 2) bias correction of NEXRAD estimates. However, in public domain, there is no Geographic Information System (GIS) software that can use geostatistical approaches to calibrate NEXRAD data using raingauge data, and automatically process NEXRAD data for hydrologic and ecological models. In this study, we developed new GIS software for NEXRAD validation and calibration (NEXRAD-VC) using raingauge data. NEXRAD-VC can automatically read in NEXRAD data in NetCDF or XMRG format, transform projection of NEXRAD data to match with raingauge data, apply different geostatistical approaches to calibrate NEXRAD data using raingauge data, evaluate performance of different calibration methods using leave-one-out cross-validation scheme, output spatial precipitation maps in ArcGIS grid format, calculate spatial average precipitation for each spatial modeling unit used by hydrologic and ecological models. The major functions of NEXRAD-VC are illustrated in the Little River Experimental Watershed (LREW) in Georgia using daily precipitation records of fifteen raingauges and NEXRAD products of five years. The validation results show that NEXRAD has a high success rate for detecting rain and no-rain events: 82.8% and 95.6%, respectively. NEXRAD estimates have high correlation with raingauge observations (correlation coefficient of 0.91), but relatively larger relative mean absolute error value of 36%. It is also worth noting that the performance of NEXRAD increases with the decreasing of rainfall variability. Three methods (Bias Adjustment method (BA), Regressing Kriging (RK), and Simple Kriging with varying local means (SKlm)) were employed to calibrate NEXRAD using raingauge data. Overall, SKlm performed the best among these methods. Compared with NEXRAD, SKlm improved the correlation coefficient to 0.96 and the relative mean absolute error to 22.8%, respectively. SKlm also increased the success rate of detection of rain and no-rain events to 87.47% and 96.05%, respectively. Further analysis of the performance of the three calibration methods and NEXRAD for daily spatial precipitation estimation shows that no one method can consistently provide better results than the other methods for each evaluation coefficient for each day. It is suggested that multiple methods be implemented to predict spatial precipitation. The NEXRAD-VC developed in this study can serve as an effective and efficient tool to batch process large amounts of NEXRAD data for hydrologic and ecological modeling. [Copyright &y& Elsevier]

Summary: Typically, flash floods are studied ex post facto in response to a major impact event. A complement to field investigations is developing a detailed database of flash flood events, including minor events and null reports (i.e., where heavy rain occurred but there was no flash flooding), based on public survey questions conducted in near-real time. The Severe hazards analysis and verification experiment (SHAVE) has been in operation at the National Severe Storms Laboratory (NSSL) in Norman, OK, USA during the summers since 2006. The experiment employs undergraduate students to analyse real-time products from weather radars, target specific regions within the conterminous US, and poll public residences and businesses regarding the occurrence and severity of hail, wind, tornadoes, and now flash floods. In addition to providing a rich learning experience for students, SHAVE has also been successful in creating high-resolution datasets of severe hazards used for algorithm and model verification. This paper describes the criteria used to initiate the flash flood survey, the specific questions asked and information entered to the database, and then provides an analysis of results for flash flood data collected during the summer of 2008. It is envisioned that specific details provided by the SHAVE flash flood observation database will complement databases collected by operational agencies (i.e., US National Weather Service Storm Data reports) and thus lead to better tools to predict the likelihood of flash floods and ultimately reduce their impacts on society. [ABSTRACT FROM AUTHOR]

Abstract: The NOAA National Weather Service has maintained an automated, centralized 0–3 h prediction system for probabilistic quantitative precipitation forecasts since 2001. This advective-statistical system (ADSTAT) produces probabilities that rainfall will exceed multiple threshold values up to 50 mm at some location within a 40-km grid box. Operational characteristics and development methods for the system are described. Although development data were stratified by season and time of day, ADSTAT utilizes only a single set of nation-wide equations that relate predictor variables derived from radar reflectivity, lightning, satellite infrared temperatures, and numerical prediction model output to rainfall occurrence. A verification study documented herein showed that the operational ADSTAT reliably models regional variations in the relative frequency of heavy rain events. This was true even in the western United States, where no regional-scale, gridded hourly precipitation data were available during the development period in the 1990s. An effort was recently launched to improve the quality of ADSTAT forecasts by regionalizing the prediction equations and to adapt the model for application in the Czech Republic. We have experimented with incorporating various levels of regional specificity in the probability equations. The geographic localization study showed that in the warm season, regional climate differences and variations in the diurnal temperature cycle have a marked effect on the predictor–predictand relationships, and thus regionalization would lead to better statistical reliability in the forecasts. [Copyright &y& Elsevier]

Abstract: This paper investigates the transport structure of surface currents around the Monterey Bay, CA region. Currents measured by radar stations around Monterey Bay indicate the presence of strong, spatial and temporal, nonlinear patterns. To understand the geometry of the flow in the bay, Lagrangian coherent structures (LCS) are computed. These structures are mobile separatrices that divide the flow into regions of qualitatively different dynamics. They provide direct information about the flow structure but are geometrically simpler than particle trajectories themselves. The LCS patterns were used to reveal the mesoscale flow conditions observed during the 2003 Autonomous Ocean Sampling Network (AOSN-II) experiment. Drifter paths from the AOSN experiment were compared to the patterns induced by the LCS computed from high-frequency radar data. We verify that the fate of the drifters can be better characterized based on the LCS than direct interpretation of the current data. This property can be exploited to optimize drifter deployment. [Copyright &y& Elsevier]

Abstract: Impact of HF radar surface-current assimilation on ocean circulation model predictions in the Monterey Bay area is studied and evaluated during the time frame of the Autonomous Ocean Sampling Network (AOSN-II) experiment (August–September 2003). In the first instance, a previously described method for assimilation of surface current data is applied to 33-h low-pass-filtered data and a non-tidal version of the circulation model. It is demonstrated that assimilation of surface velocity data significantly improves the surface and subsurface correlation of model currents with moored current observations. These results from the AOSN-II period illustrate that surface-current assimilation is beneficial even in cases for which very high-resolution (3km) atmospheric forcing is utilized. The assimilation approach is also tested with hourly, unfiltered, CODAR-type HF radar-derived surface currents within a model configuration that includes tidal forcing. It is shown, that assimilation of unfiltered (with tides) surface-current observations into the model with tides improves the sub-tidal model predictions to the level comparable with the assimilation of filtered data into the non-tidal model, which is significant with respect to options for designing real-time nowcast and forecast systems. Finally, the approach is extended and evaluated for the direct assimilation of HF radar-derived radial velocity components. The model runs that included assimilation of radials from at least two HF radar sites show better correlations with observations than the non-assimilative run, especially those runs that included radials from the Santa Cruz site. Directions of radials for that site coincide with the directions of dominant southward flow during upwelling events and the northward flow during relaxation events. Direct assimilation of radial currents extends the range of influence of the data into regions covered by only one HF radar site. [Copyright &y& Elsevier]

This research investigates the appropriate scale for watershed averaged and site specific soil moisture retrieval from high resolution radar imagery. The first approach involved filtering backscatter for input to a retrieval model that was compared against field measures of soil moisture. The second approach involved spatially averaging raw and filtered imagery in an image-based statistical technique to determine the best scale for site-specific soil moisture retrieval. Field soil moisture was measured at 1225 m2 sites in three watersheds commensurate with 7 m resolution Radarsat image acquisition. Analysis of speckle reducing block median filters indicated that 5×5 filter level was the optimum for watershed averaged estimates of soil moisture. However, median filtering alone did not provide acceptable accuracy for soil moisture retrieval on a site-specific basis. Therefore, spatial averaging of unfiltered and median filtered power values was used to generate backscatter estimates with known confidence for soil moisture retrieval. This combined approach of filtering and averaging was demonstrated at watersheds located in Arizona (AZ), Oklahoma (OK) and Georgia (GA). The optimum ground resolution for AZ, OK and GA study areas was 162 m, 310 m, and 1131 m respectively obtained with unfiltered imagery. This statistical approach does not rely on ground verification of soil moisture for validation and only requires a satellite image and average roughness parameters of the site. When applied at other locations, the resulting optimum ground resolution will depend on the spatial distribution of land surface features that affect radar backscatter. This work offers insight into the accuracy of soil moisture retrieval, and an operational approach to determine the optimal spatial resolution for the required application accuracy. [Copyright &y& Elsevier]

Scanning Light Detecting and Ranging (LiDAR), Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) were analyzed to determine (1) which of the three sensor systems most accurately predicted forest biomass, and (2) if LiDAR and SAR/InSAR data sets, jointly considered, produced more accurate, precise results relative to those same data sets considered separately. LiDAR ranging measurements, VHF–SAR cross-sectional returns, and X-and P-band cross-sectional returns and interferometric ranges were regressed with ground-estimated (from dbh) forest biomass in ponderosa pine forests in the southwestern United States. All models were cross-validated. Results indicated that the average canopy height measured by the scanning LiDAR produced the best predictive equation. The simple linear LiDAR equation explained 83% of the biomass variability (n =52 plots) with a cross-validated root mean square error of 26. 0 t/ha. Additional LiDAR metrics were not significant to the model. The GeoSAR P-band (λ =86 cm) cross-sectional return and the GeoSAR/InSAR canopy height (X–P) captured 30% of the forest biomass variation with an average predictive error of 52. 5 t/ha. A second RaDAR–FOPEN collected VHF (λ ∼7. 8 m) and cross-polarized P-band (λ =88 cm) cross-sectional returns, none of which proved useful for forest biomass estimation (cross-validated R 2 =0. 09, RMSE=63. 7 t/ha). Joint consideration of LiDAR and RaDAR measurements produced a statistically significant, albeit small improvement in biomass estimation precision. The cross-validated R 2 increased from 83% to 84% and the prediction error decreased from 26. 0 t/ha to 24. 9 t/ha when the GeoSAR X–P interferometric height is considered along with the average LiDAR canopy height. Inclusion of a third LiDAR metric, the 60th decile height, further increased the R 2 to 85% and decreased the RMSE to 24. 1 t/ha. On this 11 km2 ponderosa pine study area, LiDAR data proved most useful for predicting forest biomass. RaDAR ranging measurements did not improve the LiDAR estimates. [Copyright &y& Elsevier]

Instrumentation and methods for measuring snow properties are compared in an investigation of millimeter- to meter-scale stratigraphy in a snowpack not influenced by topography, vegetation, or a warm and variable ground surface. Field measurements were conducted within a 20×20×2 m plot at Pika Glacier, Alaska. The snow was characterized by more than 600 point measurements of density, stratigraphic mapping in 19 snow-pits, and by pulse-radar imaging along 20 cross-plot profiles. Density was measured manually and was calculated from electric permittivity, which was determined with a hand-held probe and by radar velocity analysis. Stratigraphic mapping in snow-pit walls with manual measurements of density identified comparatively few layers, suggesting a relatively homogeneous snowpack. Both the permittivity probe and the radar imaging, however, identified a larger number of layers based on vertical density contrasts. Image analysis of a back-illuminated column of snow revealed the highest level of stratigraphic complexity, identifying layers mm in thickness that extended up to 10 cm laterally. Despite minor variations in snow properties at the mm scale, major features in the vertical density profiles were laterally continuous over tens of meters. These results provide evidence for spatial homogeneity of densification processes leading to decimeter scale layering in a situation where the snowpack is not influenced by local terrain factors. In addition, these observations demonstrate that the complexity of snow stratigraphy is highly dependent upon choice of scale and measurement tool. [Copyright &y& Elsevier]

Airborne cloud radar and cloud microphysical data, collected in summertime stratus clouds off the Oregon coast, are analyzed to obtain an empirical threshold radar reflectivity above which drizzle can be expected in warm marine stratus. Such clouds, which are prevalent over the eastern subtropical oceans, have a profound impact on global climate.This study finds that the threshold reflectivity for drizzle is a strong function of height within the cloud layer. Both radar reflectivities and calculated reflectivities (based on measured droplet spectra) indicate that the threshold is most crisply defined in the lower half of the stratus cloud deck. The use of the threshold reflectivity profile to flag drizzle is illustrated by examining the dependency of radar reflectivity on cloud water content. Such dependency is non-existent in marine stratus in general, but a clear relationship emerges when drizzle cases are excluded. [Copyright &y& Elsevier]

The procedure estimating hourly rainfalls by merging radar-derived rainfalls and gauge measurements is developed and tested. It uses simple linear regression, which is complemented by the normalization and correction of distribution. The data from radar Tulsa, Oklahoma, Weather Surveillance Radar-1988 Doppler version and rain gauge data from the radar domain are used. The quality of estimates is evaluated against independent rain gauges by the root-mean-square-error, bias and correlation coefficient in dependence on the density of a gauge network. The results indicate that even a sparse gauge network (about 50 gauges, i.e. 4000 km2 per one gauge) is sufficient to improve the radar-derived rainfalls. The improvement increases with the number of gauges. [Copyright &y& Elsevier]

To aid in modeling studies over the Mississippi River Basin, we have developed an archival precipitation data set for the GEWEX Continental-Scale International Project. The data set spans from 1996–2000, a 5-year continuous period of record. Inputs for the data set are the National Reflectivity Composite that we obtained in Hierarchical Data Format. The size of the input data is 40 GB. In order to significantly reduce the size of the data set, we have developed an efficient data format that can be read from a disc faster than the input Hierarchical Data Format. We have also developed browsing tools for users of the data set. The data browser allows for fast and efficient viewing of the precipitation data and was produced using a common Geographic Information System software. Peripheral data are also included in the browser as an aid to the user. [Copyright &y& Elsevier]