Search

Urban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that precipitation can be enhanced downwind of a city, albeit other locations of precipitation enhancement have also been reported. Among the proposed mechanisms that modify the precipitation, the thermodynamic and aerodynamic processes in the urban lower atmosphere interact with the synoptic conditions and could play a key role in determining the resulting spatial variability of precipitation. In addition, these processes are intricately shaped by urban form characteristics, such as the spatial extent of the impervious land. This study aims to unravel how different urban forms impact the spatial organizations of precipitation climatology under different synoptic conditions. We use the Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation data products and analyze the hourly precipitation maps for a selected set of cities across the continental United States from the years 2015 to 2021. Results suggest that a statistically significant downwind enhancement of precipitation does exist in about four-fifths of these cities, while the magnitude is comparable to previous findings. Additionally, we find that the precipitation distribution tends to be more clustered for higher wind speed; the location for precipitation maxima is located closer to the city center under low synoptic winds but shifts towards the urban-rural interface under high wind conditions. The magnitude of downwind precipitation enhancement is highly dependent on wind directions and is positively correlated with the city size for the south, southwest, and west directions. This study provides observational proof through a cross-city analysis that the spatial pattern of urban precipitation can be attributed to the modified atmospheric processes by distinct urban forms.

The complex precipitation microphysics associated with super typhoon Lekima (2019) and its potential impacts on the consistency of multi-source datasets and radar quantitative precipitation estimation has been disentangled using a suite of in situ and remote sensing observations around the disastrous waterlogging area near the groove windward slope (GWS) of Yan Dang Mountain and Kuo Cang Mountain, China. The dynamic microphysical processes, in which breakup overwhelms over coalescence as the main outcome of the collision of precipitation particles, noticeably changes the self-consistency between radar reflectivity (ZH), differential reflectivity (ZDR) and the specific differential phase (KDP), and their consistency with theoretical counterparts simulated with surface disdrometer measurements, which are quality-controlled in terms of fall velocity characteristics of different hydrometeors to remove wind effects, hailstones and graupels. The overwhelming breakup accounts for phenomenon that high concentration rather than size contributes more to large ZH of the storm and the large deviation of attenuation-corrected ZDR from its expected values (ẐDR). As a result, although a comparable performance of ZH-based rainfall estimates R() and KDP-based rainfall estimates R(KDP) can be achieved, R(ZH, ZDR) tends to overestimate precipitation. R(ZH, ẐDR) performs the best among all rainfall estimators and it is less sensitive to the potential microphysical processes, owning to the improved self-consistency between radar-measured ZH, ZDR and KDP and their consistency with surface counterparts.

Raindrop size distribution (DSD) information is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation, especially in complex terrain or urban environments which are known for complicated rainfall mechanism and high spatial and temporal variability. In this study, the DSD characteristics of rainy seasons in the Beijing urban area are extensively investigated using 5-year DSD observations from a Parsivel2 disdrometer located at Tsinghua University. The results show that the DSD samples with rain rate < 1 mm h−1 account for more than half of total observations. The mean values of the normalized intercept parameter (log 10Nw) and the mass-weighted mean diameter (Dm) of convective rain are higher than that of stratiform rain, and there is a clear boundary between the two types of rain in terms of the scattergram of log 10Nw versus Dm. The convective rain in Beijing is neither continental nor maritime, owing to the particular location and local topography. As the rainfall intensity increases, the DSD spectra become higher and wider, but they still have peaks around diameter D∼0.5 mm. The midsize drops contribute most towards accumulated rainwater. The Dm and log 10Nw values exhibit a diurnal cycle and an annual cycle. In addition, at the stage characterized by an abrupt rise of urban heat island (UHI) intensity as well as the stage of strong UHI intensity during the day, DSD shows higher Dm values and lower log 10Nw values. The localized radar reflectivity (Z) and rain rate (R) relations (Z=aRb) show substantial differences compared to the commonly used NEXRAD relationships, and the polarimetric radar algorithms R(Kdp), R(Kdp, ZDR), and R(ZH, ZDR) show greater potential for rainfall estimation.

It is well known that the performance of radar-derived quantitative precipitation estimates greatly relies on the physical model of the raindrop size distribution (DSD) and the relation between the physical model and radar parameters. However, incorporating changing precipitation microphysics to dynamically adjust the radar reflectivity (Z) and rain rate (R) relations can be challenging for real-time applications. In this study, two adaptive radar rainfall approaches are developed based on the radar-gauge feedback mechanism using 16 S-band Doppler weather radars and 4579 surface rain gauges deployed over the Eastern JiangHuai River Basin (EJRB) in China. Although the Z–R relations in both approaches are dynamically adjusted within a single precipitation system, one is using a single global optimal (SGO) Z–R relation, whereas the other is using different Z–R relations for different storm cells identified by a storm cell identification and tracking (SCIT) algorithm. Four precipitation events featured by different rainfall microphysical characteristics are investigated to demonstrate the performances of these two rainfall mapping methodologies. In addition, the short-term vertical profile of reflectivity (VPR) clusters are extensively analyzed to resolve the storm-scale characteristics of different storm cells. The verification results based on independent gauge observations show that both rainfall estimation approaches with dynamic Z–R relations perform much better than fixed Z–R relations. The adaptive approach incorporating the SCIT algorithm and real-time gauge measurements performs best since it can better capture the spatial variability and temporal evolution of precipitation.

This paper describes observations obtained during the Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) by the US National Oceanic and Atmospheric Administration's (NOAA) Lockheed WP-3D Orion research aircraft based on the island of Barbados during the period Jan 17–Feb 11 2020. The aircraft obtained 95 hours of observations over eleven flights, many of which were coordinated with the NOAA research ship R/V Ronald H. Brown and autonomous platforms deployed from the ship. Each flight contained a mixture of sampling strategies including: high-altitude circles with frequent dropsonde deployment to characterize the large-scale environment; slow descents and ascents to measure the distribution of water vapor and its isotopic composition; stacked legs aimed at sampling the microphysical and thermodynamic state of the boundary layer; and offset straight flight legs for observing clouds and the ocean surface with remote sensing instruments and the thermal structure of the ocean with in situ sensors dropped from the plane. The characteristics of the it in situ observations, expendable devices, and remote sensing instrumentation are described, as is the processing used in deriving estimates of physical quantities. Data archived at the National Center for Environmental Information include flight-level data such as aircraft navigation and basic thermodynamic information (https://doi.org/10.25921/7jf5-wv54); high-accuracy measurements of water vapor concentration from an isotope analyzer (https://doi.org/10.25921/c5yx-7w29); profiles of sea water temperature made with Airborne eXpendable BathyThermographs (AXBTs, https://doi.org/10.25921/pe39-sx75); profiles of radar reflectivity, Doppler velocity, and spectrum width from a nadir-looking W-band (94 GHz) radar (https://doi.org/10.25921/n1hc-dc30); estimates of cloud presence, the cloud top location, and the cloud-top radar reflectivity and temperature, along with estimates of 10-m wind speed obtained from remote sensing instruments operating in the microwave and thermal infrared spectral regions (https://doi.org/10.25921/x9q5-9745); and ocean surface wave characteristics from a Wide Swath Radar Altimeter (https://doi.org/10.25921/qm06-qx04). Data are provided as netCDF files following Climate and Forecast conventions.

The operational Weather Surveillance Radar - 1988 Doppler (WSR-88D) network is an efficient tool for observing hydrometeorology processes and it forms the cornerstone of national weather forecast and warning systems. However, the observation performance of the WSR-88D network is severely hampered over the western U.S., due to 1) the radar network density is not as high as that over the eastern U.S.; 2) WSR-88D radar beams are often partially or fully blocked by the mountainous terrain in the western U.S. For example, the San Francisco Bay Area in Northern California, which supports one of the most prosperous economies in the U.S., is expected to be covered by two WSR-88D radars: KMUX and KDAX. The KMUX radar is located in the Santa Cruz Mountains at an elevation of over 1000 m above mean sea level (AMSL) compared with the densely populated valley regions which are near the sea level. Typically, the storms in Northern California have freezing levels approximately 1–2 km AMSL. As the distance from the radar increases, the KMUX radar beam can easily overshoot the mixed-phase hydrometeors in the bright band or snowflakes above the bright band, even if it is raining at the ground. The KDAX radar is located near the sea level in Davis, California. However, the KDAX radar beams are partially blocked by the Coast Ranges at low elevation angles. The coverage limitations of the KMUX and KDAX radars are further compounded by the complex precipitation microphysics as a result of land-ocean interaction in the coastal regions and orographic enhancement in the mountainous regions. As a result, it is still challenging to monitor and predict the changing atmospheric conditions using operational radars in the Bay Area, which will make the Bay Area particularly susceptible to catastrophic flooding that disrupts transportation, threatens public safety, and negatively impacts water quality. In this paper, we present an Advanced Quantitative Precipitation Information (AQPI) system built by NOAA and collaborating partners to improve monitoring and forecasting of precipitation and coastal flooding in the Bay Area. The high-frequency (i.e., C and X band) high-resolution gap-filling radars deployed as part of the AQPI program are detailed. A radar-based rainfall system is designed to improve real-time precipitation estimation over the Bay Area. The sensitivity of rainfall products on the occurrence of hydrologic extremes is investigated through a distributed hydrological model to improve the streamflow forecast. The performance of rainfall and associated hydrological impacts during the 2018-2019 and 2019-2020 winter storm seasons is quantified in the context of improving urban resiliency to natural disasters in such a complex environment.

Substantial biases exist in the Satellite Precipitation Estimates (SPE) over complex terrain regions and it has always been a challenge to quantify and correct such biases. The combination of multiple SPE and ground observations would be beneficial to improve the precipitation estimates. In this study, a flexible two-step approach is proposed by firstly reducing the systematic errors of each SPE using rain gauge observations as references, and then merging the improved multi-SPE with a Bayesian weighting model. In the 1st stage, gauge references are assumed as a generalized regression function of SPE and terrain feature. In the 2nd stage, the weights assigned to the involved SPE are calculated according to the associated performance relative to gauge references. This blending method has the ability to exert benefits from multi-SPE in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying prior distributions on the model parameters, which enables to produce posterior ensembles associated with their predictive uncertainties. The performance of the two-step blending approach is assessed using independent rain gauge observations during the warm season of 2014 in the northeastern Tibetan Plateau. Results show that the blended multi-SPE are significantly improved compared to the original individuals, especially during heavy rainfall events. This study can also be expanded as a data fusion framework in the development of high-quality precipitation products in high-cold regions characterized by complex terrain.

Raindrop size distribution (DSD) information is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation, especially in complex terrain or urban environment which is known for its complicated rainfall mechanism and high spatial and temporal variability. In this study, the DSD characteristics of rainy seasons in Beijing urban area are extensively investigated using 5-year DSD observations from a Parsivel2 disdrometer located at Tsinghua University. The results show that the DSD samples with rain rate < 1 mm h−1 account for more than half of total observations. The mean values of log10 Nw and Dm of convective rain are higher than that of stratiform rain, and there is a clear boundary between the two types of rain in terms of the scattergram of log10Nw versus Dm. The convective rain in Beijing is neither continental nor maritime owing to the particular location and local topography. As the rainfall intensity increases, the DSD spectra become higher and wider, but they still have peaks around diameter D ~ 0.5 mm. The midsize drops contribute most towards accumulated rainwater. The Dm and log10Nw values show a diurnal cycle and an annual cycle. In addition, DSD shows higher Dm values and lower log10Nw values during the periods of strong urban heat island (UHI) effect and UHI up stage of a day, and the same in July and August. The localized radar reflectivity (Z) and rain rate (R) relations (Z = aRb) show substantial differences compared to the commonly used NEXRAD relationships. And the polarimetric radar algorithms R(Kdp), R(Kdp, ZDR), and R(ZH, ZDR) show greater potential for rainfall estimation.

Flooding is one of the most common natural hazards that produce substantial loss of life and property. The QPE products that are derived at high spatiotemporal resolution, which is enabled by the deployment of a dense radar network, have the potential to improve the prediction of flash-flooding threats when coupled with hydrological models. The US National Science Foundation Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is dedicated to revolutionizing our ability to observe, understand, predict, and respond to hazardous weather events, especially in the lower atmosphere. CASA's technology enables precipitation observation close to the ground and QPE is one of the important products generated by the system. This paper describes the CASA QPE system built on the various underlying technologies of networked X-band radar systems providing high-resolution (in space and time) measurements, using the rainfall products from the radar. Evaluation of the networked rainfall product using 5 yr of data from the CASA IP-1 test bed is presented. Cross validation of the product using 5 yr of data with a gauge network is also provided. The validation shows the excellent performance of the CASA QPE system with a standard error of 25% and a low bias of 3.7%. Examples of various CASA rainfall products including instantaneous and hourly rainfall accumulations are shown.