Remote Sensing

Research

20 pages, 2442 KiB

Open AccessArticle

An Investigation of NEXRAD-Based Quantitative Precipitation Estimates in Alaska

by Brian R. Nelson, Olivier P. Prat and Ronald D. Leeper

Remote Sens. 2021, 13(16), 3202; https://doi.org/10.3390/rs13163202 - 12 Aug 2021

Cited by 2 | Viewed by 2929

Precipitation estimation by weather radars in Alaska is challenging. In this study, we investigate National Weather Service (NWS) precipitation products that are produced from the seven NEXRAD radar sites in Alaska. The NWS precipitation processing subsystem generates stages of data at each NEXRAD [...] Read more.

Precipitation estimation by weather radars in Alaska is challenging. In this study, we investigate National Weather Service (NWS) precipitation products that are produced from the seven NEXRAD radar sites in Alaska. The NWS precipitation processing subsystem generates stages of data at each NEXRAD site which are then input to the weather forecast office to generate a regionwide precipitation product. Data from the NEXRAD sites and the operational rain gauges in the weather forecast region are used to produce this regionwide product that is then sent to the National Centers for Environmental Prediction (NCEP) to be included in the NCEP Stage IV distribution. The NCEP Stage IV product for Alaska has been available since 2017. We use the United States Climate Reference Network (USCRN) data from Alaska to compare to the NCEP Stage IV data. Given that the USCRN can be used in the production of the NCEP Stage IV data for Alaska, we also used the NEXRAD Digital Precipitation Array (DPA) that is generated at the site for comparison of the radar-only products. Comparing the NEXRAD-based data from Alaska to the USCRN gauge estimates using the USCRN site information on air temperature, we are able to condition the analysis based on the hourly or 6-hourly average air temperature. The estimates in the frozen phase of precipitation largely underestimate as compared to the gauge, and the correlation is low with larger errors as compared to other phases of precipitation. In the mixed phase the underestimation of precipitation improves, but the correlation is still low with relatively large errors as compared to the rain phases of precipitation. The difficulties in precipitation estimation in cold temperatures are well known and we show the evaluation for the NCEP Stage IV regional data for Alaska and the NEXRAD site specific Digital Precipitation Array (DPA) data. Results show the challenges of estimating mixed-phase and frozen precipitation. However, the DPA data shows somewhat better performance in the mixed precipitation phase, which suggests that the NWS Precipitation Processing Subsystem (PPS) is tuned to the climatology as it relates to precipitation in Alaska. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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18 pages, 5529 KiB

Open AccessArticle

Observation of Cloud Base Height and Precipitation Characteristics at a Polar Site Ny-Ålesund, Svalbard Using Ground-Based Remote Sensing and Model Reanalysis

by Acharya Asutosh, Sourav Chatterjee, M.P. Subeesh, Athulya Radhakrishnan and Nuncio Murukesh

Remote Sens. 2021, 13(14), 2808; https://doi.org/10.3390/rs13142808 - 17 Jul 2021

Cited by 4 | Viewed by 3787

Abstract

Clouds play a significant role in regulating the Arctic climate and water cycle due to their impacts on radiative balance through various complex feedback processes. However, there are still large discrepancies in satellite and numerical model-derived cloud datasets over the Arctic region due [...] Read more.

Clouds play a significant role in regulating the Arctic climate and water cycle due to their impacts on radiative balance through various complex feedback processes. However, there are still large discrepancies in satellite and numerical model-derived cloud datasets over the Arctic region due to a lack of observations. Here, we report observations of cloud base height (CBH) characteristics measured using a Vaisala CL51 ceilometer at Ny-Ålesund, Svalbard. The study highlights the monthly and seasonal CBH characteristics at the location. It is found that almost 40% of the lowest CBHs fall within a height range of 0.5–1 km. The second and third cloud bases that could be detected by the ceilometer are mostly concentrated below 3 km during summer but possess more vertical spread during the winter season. Thin and low-level clouds appear to be dominant during the summer. Low-level clouds are found to be dominant and observed in 76% of cases. The mid and high-level clouds occur in ~16% and ~7% of cases, respectively. Further, micro rain radar (MRR2) observed enhanced precipitation and snowfall events during the winter and spring which are found to be associated with the lowest CBHs within 2 km from the ground. The frontal process associated with synoptic-scale meteorological conditions explains the variabilities in CBH and precipitation at the observation site when compared for two contrasting winter precipitation events. The findings of the study could be useful for model evaluation of cloud precipitation relationships and satellite data validation in the Arctic environment. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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19 pages, 40305 KiB

Open AccessArticle

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

by Zeinab Takbiri, Lisa Milani, Clement Guilloteau and Efi Foufoula-Georgiou

Remote Sens. 2021, 13(13), 2641; https://doi.org/10.3390/rs13132641 - 5 Jul 2021

Cited by 4 | Viewed by 3055

Abstract

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and [...] Read more.

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE)

\leq 100

kg m^{- 2}

) and low values of cloud liquid water path (LWP 100–150

g m^{- 2}

), the scattering of light snowfall (intensities

\leq 0.5

mm h^{- 1}

) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200

kg m^{- 2}

and the LWP is greater than 100–150

g m^{- 2}

, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200

kg m^{- 2}

and LWP is lower than 100–150

g m^{- 2}

, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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28 pages, 15561 KiB

Open AccessArticle

A Study of Two Impactful Heavy Rainfall Events in the Southern Appalachian Mountains during Early 2020, Part II; Regional Overview, Rainfall Evolution, and Satellite QPE Utility

by Douglas Miller, Malarvizhi Arulraj, Ralph Ferraro, Christopher Grassotti, Bob Kuligowski, Shuyan Liu, Veljko Petkovic, Shaorong Wu and Pingping Xie

Remote Sens. 2021, 13(13), 2500; https://doi.org/10.3390/rs13132500 - 26 Jun 2021

Cited by 2 | Viewed by 2724

Abstract

Two heavy rainfall events occurring in early 2020 brought flooding, flash flooding, strong winds, and tornadoes to the southern Appalachian Mountains. Part I of the study examined large-scale atmospheric contributions to the atmospheric river-influenced events and subsequent societal impacts. Contrary to expectations based [...] Read more.

Two heavy rainfall events occurring in early 2020 brought flooding, flash flooding, strong winds, and tornadoes to the southern Appalachian Mountains. Part I of the study examined large-scale atmospheric contributions to the atmospheric river-influenced events and subsequent societal impacts. Contrary to expectations based on previous work in this region, the event having a lower event accumulation and shorter duration resulted in a greater number of triggered landslides and prolonged downstream flooding outside of the mountains. One purpose of this study (Part II) is to examine the local atmospheric conditions contributing to the rather unusual surface response to the shorter duration heavy rainfall event of 12–13 April 2020. A second purpose of this study is to investigate the utility of several spaced-based QPE and vertical atmospheric profile methods in illuminating some of the atmospheric conditions unique to the April event. The embedded mesoscale convective elements in the warm sector of the April event were larger and of longer duration than of the other event in February 2020, leading to sustained periods of convective rain rates. The environment of the April event was convectively unstable, and the resulting available potential energy was sustained by relatively dry airstreams at the 700 hPa level, continuously overriding the moist air stream at low levels attributed to an atmospheric river. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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29 pages, 27445 KiB

Open AccessArticle

A Study of Two Impactful Heavy Rainfall Events in the Southern Appalachian Mountains during Early 2020, Part I; Societal Impacts, Synoptic Overview, and Historical Context

by Douglas Miller, John Forsythe, Sheldon Kusselson, William Straka III, Jifu Yin, Xiwu Zhan and Ralph Ferraro

Remote Sens. 2021, 13(13), 2452; https://doi.org/10.3390/rs13132452 - 23 Jun 2021

Cited by 5 | Viewed by 3740

Abstract

Two heavy rainfall events occurring in early 2020 brought flooding, flash flooding, strong winds and tornadoes to the southern Appalachian Mountains. The atmospheric river-influenced events qualified as extreme (top 2.5%) rain events in the archives of two research-grade rain gauge networks located in [...] Read more.

Two heavy rainfall events occurring in early 2020 brought flooding, flash flooding, strong winds and tornadoes to the southern Appalachian Mountains. The atmospheric river-influenced events qualified as extreme (top 2.5%) rain events in the archives of two research-grade rain gauge networks located in two different river basins. The earlier event of 5–7 February 2020 was an event of longer duration that caused significant flooding in close proximity to the mountains and had the higher total accumulation observed by the two gauge networks, compared to the later event of 12–13 April 2020. However, its associated downstream flooding response and number of landslides (two) were muted compared to the April event (21). The purpose of this study is to understand differences in the surface response of the two events, primarily by examining the large-scale weather pattern and available space-based observations. Both storms were preceded by anticyclonic Rossby wave breaking events that led to a highly amplified 500 hPa wave during the February storm (a broad continent-wide 500 hPa cyclone during the April storm) in which the accompanying low-level cyclone moved slowly (rapidly). Model analyses and space-based water vapor observations of the two events indicated a deep sub-tropical moisture source during the February storm (converging sub-tropical low-level moisture streams and a dry mid-tropospheric layer during the April storm). Systematic differences of environmental stability were reflected in differences of storm-averaged rain rate intensity, with large-scale atmospheric structures favoring higher intensities during the April storm. Space-based observations of post-storm surface conditions suggested antecedent soil moisture conditioned by rainfall of the February event made the widespread triggering of landslides possible during the higher intensity rains of the April event, a period exceeding the 30 day lag explored in Miller et al. (2019). Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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20 pages, 4583 KiB

Open AccessArticle

Measurements of Rainfall Rate, Drop Size Distribution, and Variability at Middle and Higher Latitudes: Application to the Combined DPR-GMI Algorithm

by Viswanathan Bringi, Mircea Grecu, Alain Protat, Merhala Thurai and Christian Klepp

Remote Sens. 2021, 13(12), 2412; https://doi.org/10.3390/rs13122412 - 20 Jun 2021

Cited by 7 | Viewed by 3230

Abstract

The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth’s global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager [...] Read more.

The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth’s global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager which provide measurements to retrieve the drop size distribution (DSD) and rain rates using a Combined Radar-Radiometer Algorithm (CORRA). Our objective is to validate key assumptions and parameterizations in CORRA and enable improved estimation of precipitation products, especially in the middle-to-higher latitudes in both hemispheres. The DSD parameters and statistical relationships between DSD parameters and radar measurements are a central part of the rainfall retrieval algorithm, which is complicated by regimes where DSD measurements are abysmally sparse (over the open ocean). In view of this, we have assembled optical disdrometer datasets gathered by research vessels, ground stations, and aircrafts to simulate radar observables and validate the scattering lookup tables used in CORRA. The joint use of all DSD datasets spans a large range of drop concentrations and characteristic drop diameters. The scaling normalization of DSDs defines an intercept parameter N_W, which normalizes the concentrations, and a scaling diameter D_m, which compresses or stretches the diameter coordinate axis. A major finding of this study is that a single relationship between N_W and D_m, on average, unifies all datasets included, from stratocumulus to heavier rainfall regimes. A comparison with the N_W–D_m relation used as a constraint in versions 6 and 7 of CORRA highlights the scope for improvement of rainfall retrievals for small drops (D_m < 1 mm) and large drops (D_m > 2 mm). The normalized specific attenuation–reflectivity relationships used in the combined algorithm are also found to match well the equivalent relationships derived using DSDs from the three datasets, suggesting that the currently assumed lookup tables are not a major source of uncertainty in the combined algorithm rainfall estimates. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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32 pages, 10019 KiB

Open AccessArticle

Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset

by F. Joseph Turk, Sarah E. Ringerud, Andrea Camplani, Daniele Casella, Randy J. Chase, Ardeshir Ebtehaj, Jie Gong, Mark Kulie, Guosheng Liu, Lisa Milani, Giulia Panegrossi, Ramon Padullés, Jean-François Rysman, Paolo Sanò, Sajad Vahedizade and Norman B. Wood

Remote Sens. 2021, 13(12), 2264; https://doi.org/10.3390/rs13122264 - 9 Jun 2021

Cited by 22 | Viewed by 6704

Abstract

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the [...] Read more.

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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25 pages, 9094 KiB

Open AccessArticle

The Precipitation Imaging Package: Phase Partitioning Capabilities

by Claire Pettersen, Larry F. Bliven, Mark S. Kulie, Norman B. Wood, Julia A. Shates, Jaclyn Anderson, Marian E. Mateling, Walter A. Petersen, Annakaisa von Lerber and David B. Wolff

Remote Sens. 2021, 13(11), 2183; https://doi.org/10.3390/rs13112183 - 3 Jun 2021

Cited by 13 | Viewed by 3472

Abstract

Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. [...] Read more.

Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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26 pages, 15958 KiB

Open AccessArticle

Performance of the IMERG Precipitation Products over High-latitudes Region of Finland

by Mohammed T. Mahmoud, Safa A. Mohammed, Mohamed A. Hamouda, Miikka Dal Maso and Mohamed M. Mohamed

Remote Sens. 2021, 13(11), 2073; https://doi.org/10.3390/rs13112073 - 25 May 2021

Cited by 9 | Viewed by 2515

Abstract

Highly accurate and real-time estimation of precipitation over large areas remains a fundamental challenge for the hydrological and meteorological community. This is primarily attributed to the high heterogeneity of precipitation across temporal and spatial scales. Rapid developments in remote sensing technologies have made [...] Read more.

Highly accurate and real-time estimation of precipitation over large areas remains a fundamental challenge for the hydrological and meteorological community. This is primarily attributed to the high heterogeneity of precipitation across temporal and spatial scales. Rapid developments in remote sensing technologies have made the quantitative measurement of precipitation by satellite sensors a significant data source. The Global Precipitation Measurement (GPM) mission makes precipitation data with high temporal and spatial resolutions available to different users. The objective of this study is to evaluate the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) V06 (Early, Late, and Final) satellite precipitation products (SPPs) at high latitudes. Ground-based observation data across Finland were used as a reference and compared with IMERG data from 2014 to 2019. Three aspects were evaluated: the spatial coverage of the satellite estimates over Finland; the accuracy of the satellite estimates at various temporal scales (half-hourly, daily, and monthly); and the variation in the performance of SPPs over different spatial regions. The results showed that IMERG SPPs can be used with high confidence over Southern, Eastern, and Western Finland. These SPPs can be used with caution over the region of the historical province of Oulu but are not recommended for higher latitudes over Lapland. In general, the IMERG-Final SPP performed the best, and it is recommended for use because of its low number of errors and high correlation with ground observation. Furthermore, this SPP can be used to complement or substitute ground precipitation measurements in ungauged and poorly gauged regions in Southern Finland. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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12 pages, 50810 KiB

Open AccessArticle

Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

by Lisa Milani and Norman B. Wood

Remote Sens. 2021, 13(11), 2041; https://doi.org/10.3390/rs13112041 - 21 May 2021

Cited by 14 | Viewed by 3095

Abstract

Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have [...] Read more.

Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat’s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by −10.16% and the mean snowfall rate changes by −8.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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21 pages, 1191 KiB

Open AccessArticle

Drop Size Distribution Variability in Central Argentina during RELAMPAGO-CACTI

by Candela Casanovas, Paola Salio, Victoria Galligani, Brenda Dolan and Stephen W. Nesbitt

Remote Sens. 2021, 13(11), 2026; https://doi.org/10.3390/rs13112026 - 21 May 2021

Cited by 6 | Viewed by 3135

Abstract

The Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions Experiment Proposal (CACTI) field campaigns provided an unprecedented thirteen-disdrometer dataset in Central Argentina during the Intensive (IOP, 15 November to 15 [...] Read more.

The Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions Experiment Proposal (CACTI) field campaigns provided an unprecedented thirteen-disdrometer dataset in Central Argentina during the Intensive (IOP, 15 November to 15 December 2018) and Extended (EOP, 15 October 2018 to 30 April 2019) Observational Periods. The drop size distribution (DSD) parameters and their variability were analyzed across the region of interest, which was divided into three subregions characterized by the differing proximity to the Sierras de Córdoba (SDC), in order to assess the impact of complex terrain on the DSD parameters. A rigorous quality control of the data was first performed. The frequency distributions of DSD-derived parameters were analyzed, including the normalized intercept parameter (log

N_{w}

), the mean volume diameter (

D_{0}

), the mean mass diameter (

D_{m}

), the shape parameter (

μ

), the liquid water content (LWC), and the rain rate (R). The region closest to the SDC presented higher values of log

N_{w}

, lower

D_{0}

, and higher

μ

, while the opposite occurred in the farthest region, i.e., the concentration of small drops decreased while the concentration of bigger drops increased with the distance to the east of the SDC. Furthermore, the region closest to the SDC showed a bimodal distribution of

D_{0}

: the lower values of

D_{0}

were associated with higher values of log

N_{w}

and were found more frequently during the afternoon, while the higher

D_{0}

were associated with lower log

N_{w}

and occurred more frequently during the night. The data were analyzed in comparison to the statistical analysis of Dolan et al. 2018 and sorted according to the classification proposed in the cited study. The log

N_{w}

-

D_{0}

and LWC-

D_{0}

two-dimensional distributions allowed further discussion around the applicability of other mid-latitude and global precipitation classification schemes (startiform/convection) in the region of interest. Finally, three precipitation case studies were analyzed with supporting polarimetric radar data in order to relate the DSD characteristics to the precipitation type and the microphysical processes involved in each case. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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20 pages, 7956 KiB

Open AccessArticle

Validation of Satellite-Based Precipitation Products from TRMM to GPM

by Jianxin Wang, Walter A. Petersen and David B. Wolff

Remote Sens. 2021, 13(9), 1745; https://doi.org/10.3390/rs13091745 - 30 Apr 2021

Cited by 41 | Viewed by 3554

Abstract

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) [...] Read more.

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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13 pages, 4851 KiB

Open AccessArticle

Assessing the Impact of Light/Shallow Precipitation Retrievals from Satellite-Based Observations Using Surface Radar and Micro Rain Radar Observations

by Chris Kidd, Edward Graham, Tim Smyth and Michael Gill

Remote Sens. 2021, 13(9), 1708; https://doi.org/10.3390/rs13091708 - 28 Apr 2021

Cited by 10 | Viewed by 2558

Abstract

The accurate representation of precipitation across the Earth’s surface is crucial to furthering our knowledge and understanding of the Earth System and its component processes. Precipitation poses a number of challenges, particularly due to the variability of precipitation over time and space and [...] Read more.

The accurate representation of precipitation across the Earth’s surface is crucial to furthering our knowledge and understanding of the Earth System and its component processes. Precipitation poses a number of challenges, particularly due to the variability of precipitation over time and space and whether it falls as snow or rain. While conventional measures of precipitation are reasonably good at the location of their measurement, their distribution across the Earth’s surface is uneven with some regions having no surface measurements. Spaceborne sensors have the capability of providing regular observations across the Earth’s surface that can provide estimates of precipitation. However, the estimation of precipitation from satellite observations is not necessarily straightforward. Visible and/or infrared techniques rely upon imprecise cloud-top to surface precipitation relationships, while the sensitivity of passive microwave techniques to different precipitation types is not consistent. Active microwave (radar) observations provide the most direct satellite measurements of precipitation but cannot provide estimates close to the surface and are generally not sufficiently sensitive to resolve light precipitation. This is particularly problematic at mid to high latitudes, where light and/or shallow precipitation dominates. This paper compares measurements made by ground-based weather radars, Micro Rain Radars and the spaceborne Dual-frequency Precipitation Radar to study both light precipitation intensity and shallow precipitation occurrence and to assess their impact on satellites retrievals of precipitation at the mid to high latitudes. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)

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