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Precipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor's channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems. Significance Statement: To understand how the accuracy of satellite precipitation depends on weather conditions, we compare the satellite estimates of precipitation against ground radars in the United States, using cloud regimes as a proxy for different recurring atmospheric systems. Consistent with previous studies, we found that errors in the satellite precipitation vary under different regimes. Satellite precipitation is, reassuringly, more accurate for storm systems that produce intense precipitation. However, in systems that produce weak or isolated precipitation, the errors are larger due to retrieval limitations. These findings highlight the important role of atmospheric states on the accuracy of satellite precipitation and the potential of cloud regimes for categorizing the global atmosphere. [ABSTRACT FROM AUTHOR]

Two years (2018–2019) of different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States (30°–50°N & 105°–135°W) using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non‐orographic events. NCEP Stage‐IV product is used as a reference. All of the evaluated products (radar, microwave imager/sounder, and infrared) show more significant errors for the orographic than the non‐orographic events. The Global Precipitation Mission (GPM) products; Dual‐frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), and GPM Microwave Imager (GMI), and Microwave Humidity Sounder (MHS), severely underestimate the precipitation rates, especially for heavy precipitation (>4 mm/day), whereas infrared precipitation (used in the Integrated Multi‐satellite Retrievals for GPM; IMERG‐IR) and a reanalysis product (ERA5) show relatively better estimation. It was found that the amount of precipitation over the reference (rate BIAS) varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm‐season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (<10°C) and lower TPW (<15 mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non‐orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20°C), where satellite products tend to overestimate precipitation amounts. Key Points: Several satellite‐based precipitation products are compared in relation with orographic enhancementSatellite‐based precipitation BIAS shows a seasonal patternPrecipitation BIAS is associated with the surface temperatures and total precipitable water [ABSTRACT FROM AUTHOR]

Near‐term ecological forecasting has the potential to mitigate negative impacts of human modifications on wildlife by directing efficient action through relevant and timely predictions. We used the U.S. avian migration system to highlight ecological forecasting applications for aeroconservation. We used millions of observations from 143 weather surveillance radars to construct and evaluate a migration forecasting system for nocturnal bird migration over the contiguous United States. We identified the number of nights of mitigation required to reduce the risk of aerial hazards to 50% of avian migrants passing a given area in spring and autumn based on dynamic forecasts of migration activity. We also investigated an alternative approach, that is, employing a fixed conservation strategy based on time windows that historically capture 50% of migratory passage. In practice, during both spring and autumn, dynamic forecasts required fewer action nights compared with fixed window selection at all locations (spring: mean of 7.3 more alert days; fall: mean of 12.8 more alert days). This pattern resulted in part from the pulsed nature of bird migration captured in the radar data, where the majority (54.3%) of birds move on 10% of a migration season's nights. Our results highlight the benefits of near‐term ecological forecasting and the potential advantages of dynamic mitigation strategies over static ones, especially in the face of increasing risks to migrating birds from light pollution, wind energy infrastructure, and collisions with structures. [ABSTRACT FROM AUTHOR]

A new dual-polarization (DP) radar synthetic quantitative precipitation estimation (QPE) product was developed using a combination of specific attenuation A, specific differential phase KDP, and reflectivity Z to calculate the precipitation rate R. Specific attenuation has advantages of being insensitive to systematic biases in Z and differential reflectivity ZDR due to partial beam blockage, attenuation, and calibration while more linearly related to R than other radar variables. However, the R(A) relationship is not applicable in areas containing ice. Therefore, the new DP QPE applies R(A) in areas where radar is observing pure rain, R(KDP) in regions potentially containing hail, and R(Z) elsewhere. Further, an evaporation correction was applied to minimize false light precipitation related to virga. The new DP QPE was evaluated in real time over the conterminous United States and showed significant improvements over previous radarQPE techniques that were based solely on R(Z) relationships. The improvements included reduced dry biases in heavy to extreme precipitation during the warm season. The new DP QPE also reduced errors and spatial discontinuities in regions impacted by partial beam blockage. Further, the new DP QPE reduced wet bias for scattered light precipitation in the southwest and north central United States where there is significant boundary layer evaporation. [ABSTRACT FROM AUTHOR]

Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s-1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur. [ABSTRACT FROM AUTHOR]

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]

The article focuses on the need to improve the efficacy of wide area surveillance (WAS) in order for the U.S. armed forces to get consistent and most powerful information for enabling field operations and policy decisions. It seeks to improve the condition in advancing surveillance tradecraft by defining principles. It looks primarily on motion intelligence systems like wide area motion imagery and ground-scanning moving target indicator radars.

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]

• 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]

This study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive Z- R relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed significant improvements over the current operational radar QPE (43% reduction in bias and 30% reduction in root-mean-square error) when compared with gauges. In addition, the new technique minimizes various radar artifacts and produces a spatially continuous rainfall product. Such continuity is important for accurate hydrological model predictions. The new technique is computationally efficient and can be easily transitioned into operations. One of the largest remaining challenges is obtaining accurate radar QPE over the windward slopes of significant mountain ranges, where low-level orographic enhancement of precipitation is not resolved by the operational radars leading to underestimation. Additional high-resolution and near-surface radar observations are necessary for more accurate radar QPE over these regions. [ABSTRACT FROM AUTHOR]

Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11- μm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD > 0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions. [ABSTRACT FROM AUTHOR]

Quantitative real-time observations of a tsunami have been limited to deep-water, pressure-sensor observations of changes in the sea surface elevation and observations of sea level fluctuations at the coast, which are essentially point measurements. Constrained by these data, models have been used for predictions and warning of the arrival of a tsunami, but to date no system exists for local detection of an actual incoming wave with a significant warning capability. Networks of coastal high frequency (HF)-radars are now routinely observing surface currents in many countries. We report here on an empirical method for the detection of the initial arrival of a tsunami, and demonstrate its use with results from data measured by fourteen HF radar sites in Japan and USA following the magnitude 9.0 earthquake off Sendai, Japan, on 11 March 2011. The distance offshore at which the tsunami can be detected, and hence the warning time provided, depends on the bathymetry: the wider the shallow continental shelf, the greater this time. We compare arrival times at the radars with those measured by neighboring tide gauges. Arrival times measured by the radars preceded those at neighboring tide gauges by an average of 19 min (Japan) and 15 min (USA) The initial water-height increase due to the tsunami as measured by the tide gauges was moderate, ranging from 0.3 to 2 m. Thus it appears possible to detect even moderate tsunamis using this method. Larger tsunamis could obviously be detected further from the coast. We find that tsunami arrival within the radar coverage area can be announced 8 min (i.e., twice the radar spectral time resolution) after its first appearance. This can provide advance warning of the tsunami approach to the coastline locations. [ABSTRACT FROM AUTHOR]

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]

A modified scattering model-based speckle filter (SMBSF) based on the spatial proximity principle was applied to the analysis of phased array type L-band synthetic aperture radar (PALSAR) polarimetric data in the coastal environment of North Carolina, USA. The modified filter preserved polarimetric characteristics and further reduced speckle noise qualitatively and quantitatively. Classification accuracy using the SAR data filtered by the modified filter was improved, especially for the forest class. [ABSTRACT FROM AUTHOR]

This paper describes satellite data collected as part of the 2002/03 Cold Land Processes Experiment (CLPX). These data include multispectral and hyperspectral optical imaging, and passive and active microwave observations of the test areas. The CLPX multispectral optical data include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Multi-angle Imaging Spectroradiometer (MISR). The spaceborne hyperspectral optical data consist of measurements acquired with the NASA Earth Observing-1 (EO-1) Hyperion imaging spectrometer. The passive microwave data include observations from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS; AMSR-E). Observations from the Radarsat synthetic aperture radar and the SeaWinds scatterometer flown on QuikSCAT make up the active microwave data. [ABSTRACT FROM AUTHOR]

The detection and mapping of canopy disturbance caused by natural events such as cyclones in tropical rainforests is important for monitoring and understanding rainforest dynamics and recovery. In February 1999 tropical cyclone Rona crossed the coastline of Far North Queensland, Australia, causing significant damage to the rainforest canopy in some areas. This paper examines the application of multitemporal canopy digital elevation models (DEMs) generated by a NASA-operated airborne radar mapping system called TOPSAR in 1996 and 2000, for detection of canopy disturbance caused by cyclone damage. Canopy damage was mapped here by identifying areas with a significant decrease in canopy height estimated from the difference of the 1996 and 2000 TOPSAR DEMs. Conventional aerial photographs, flown shortly after the cyclone (March 1999), were used to validate the resulting map of cyclone damaged rainforest canopy. The results showed that the DEM-derived canopy damage map performed reasonably well when comparing the spatial distribution and size of damaged areas, while taking into account the time difference between cyclone damage and the second radar acquisition. Some errors were encountered on the steeper slopes which were related to terrain distortions inherent in radar images of steep terrain. The height accuracy of the DEM was close to the depth of some of the gaps being mapped, which also contributed to errors. Even so, the results demonstrate there may be potential for weather-independent, regional-scale mapping of forest canopy change from imaging radar that is not always possible from traditional optical means of measuring canopy elevation, for example, airborne laser data and stereo aerial photography. This may be possible in relatively flat areas, provided the second radar acquisition occurs within months of the damaging event. Further algorithm refinement is required to improve its robustness and the range of topographies where this approach can provide reliable estimates of the extent of canopy disturbance. [ABSTRACT FROM AUTHOR]

The capability to retrieve cloud base heights was developed under the US National Polar‐orbiting Operational Environmental Satellite System (NPOESS) programme as one of 27 data products to be created from data collected by the Visible Infrared Imager Radiometer Suite (VIIRS). First launch of the VIIRS sensor, which is the high‐resolution Earth imager of the NPOESS programme, comes on National Aeronautics & Space Administration's (NASA) NPOESS Preparatory Project (NPP). In preparation for this launch, extensive testing of the VIIRS cloud algorithms was completed to verify that product performance will satisfy system requirements before the cloud algorithms were hosted in the NPOESS ground processing centre. The approach taken to retrieve cloud base height converts cloud optical properties into a geometric thickness which is then subtracted from the cloud top height. Performance of the cloud base height algorithms has been verified recently using MODIS data, together with temporarily and spatial coincident observations of cloud thickness values made with the millimetre cloud radar operated at the US Department of Energy Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma. Of particular significance is the clear demonstration that both cloud optical properties and cloud base heights are retrieved accurately during night‐time conditions with the VIIRS algorithms, since neither of these products is currently produced by the NASA EOS programme. Based upon these analyses, the VIIRS cloud algorithms are expected to satisfy NPOESS requirements, making VIIRS the first operational satellite sensor capable of retrieving three‐dimensional cloud fields. [ABSTRACT FROM AUTHOR]

The Mid-Canada Radar Line (MCRL) was built during the 1950s in response to the perceived threat of a Soviet nuclear attack over the Arctic. The MCRL was an entirely Canadian project, consisting of 98 radar stations that stretched across the 55th parallel from Dawson Creek, British Columbia, to Hopedale, Labrador. Seventeen MCRL sites were located in Ontario, and by 1965, all had been closed for strategic and economic reasons. Since these sites were improperly decommissioned, they have become point sources of contaminants in northern Canada. In 2001, MCRL Site 050 was remediated. The Fort Albany First Nation (located near Site 050), the Department of National Defence, and the Ontario Ministry of Natural Resources had formed a "partnership" to undertake this. We determined that from an Aboriginal perspective, a true partnership (as we define it) did exist between these organizations; a partnership based on the essential elements of respect, equity, and empowerment. We show that these cornerstones of a true partnership were present in the initial documents that discussed remediation of this site. This evaluation will provide insight, guidance, and a potential framework to benefit future partnership endeavours, helping to foster stronger collaborative relationships between Aboriginal organizations and governments at all levels, especially with respect to the remediation of abandoned radar line sites. [ABSTRACT FROM AUTHOR]

The article underscores the need to make the existence of radar systems and wind farms compatible. It states that the spinning blades of wind turbines are known to intervene with defence and weather radar systems. Relative to this concern, the United States and Great Britain have sought to limit the establishment of wind farms. JASONs, a group of academic researchers that advises U. S. defence agencies on several issues, said that the U. S. government would be better off working to create regulations and new technologies that can amend the problems between radar systems and wind farms.