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F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard

Abstract

A fully global satellite-based precipitation estimate that can transition across the changing Earth surface and complex land/water conditions is an important capability for many hydrological applications, and for independent evaluation of the precipitation derived from weather and climate models. This capability is inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate primarily from a variety of wide-swath passive microwave (MW) imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive MW measurements is much more variable for land surfaces, with varying sensitivities to near-surface precipitation. The NASA–JAXA Global Precipitation Measurement (GPM) spacecraft (2014–present) is equipped with a dual-frequency precipitation radar and a multichannel passive MW imaging radiometer specifically designed for precipitation measurement, covering substantially more land area than its predecessor Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive MW precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passive MW imagers and sounders. With over 6 years of increased land surface coverage provided by GPM, new insight has been gained into the nature of the microwave surface emissivity over land and ice/snow-covered surfaces, leading to improvements in a number of physically and semiphysically based precipitation retrieval techniques that adapt to variable Earth surface conditions. In this manuscript, the workings and capabilities of several of these approaches are highlighted.

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David T. Bolvin, George J. Huffman, Eric J. Nelkin, and Jackson Tan

Abstract

Satellite-based precipitation estimates provide valuable information where surface observations are not readily available, especially over the large expanses of the ocean where in situ precipitation observations are very sparse. This study compares monthly precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG) with gauge observations from 37 low-lying atolls from the Pacific Rainfall Database for the period June 2000–August 2020. Over the analysis period, IMERG estimates are slightly higher than the atoll observations by 0.67% with a monthly correlation of 0.68. Seasonally, DJF shows excellent agreement with a near-zero bias, while MAM shows IMERG is low by 4.6%, and JJA is high by 1.2%. SON exhibits the worst performance, with IMERG overestimating by 6.5% compared to the atolls. The seasonal correlations are well contained in the range 0.67–0.72, with the exception of SON at 0.62. Furthermore, SON has the highest RMSE at 4.70 mm day−1, making it the worst season for all metrics. Scatterplots of IMERG versus atolls show IMERG, on average, is generally low for light precipitation accumulations and high for intense precipitation accumulations, with best agreement at intermediate rates. Seasonal variations exist at light and intermediate rate accumulations, but IMERG consistently overestimates at intense precipitation rates. The differences between IMERG and atolls vary over time but do not exhibit any discernable trend or dependence on atoll population. The PACRAIN atoll gauges are not wind-loss corrected, so application of an appropriate adjustment would increase the precipitation amounts compared to IMERG. These results provide useful insight to users as well as valuable information for future improvements to IMERG.

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Mengye Chen, Zhi Li, Shang Gao, Xiangyu Luo, Oliver E. J. Wing, Xinyi Shen, Jonathan J. Gourley, Randall L. Kolar, and Yang Hong

Abstract

Because climate change will increase the frequency and intensity of precipitation extremes and coastal flooding, there is a clear need for an integrated hydrology and hydraulic system that has the ability to model the hydrologic conditions over a long period and the flow dynamic representations of when and where the extreme hydrometeorological events occur. This system coupling provides comprehensive information (flood wave, inundation extents, and depths) about coastal flood events for emergency management and risk minimization. This study provides an integrated hydrologic and hydraulic coupled modeling system that is based on the Coupled Routing and Excessive Storage (CREST) model and the Australia National University-Geophysics Australia (ANUGA) model to simulate flood. Forced by the near-real-time Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimates, this integrated modeling system was applied during the 2017 Hurricane Harvey event to simulate the streamflow, the flood extent, and the inundation depth. The results were compared with postevent high-water-mark survey data and its interpolated flood extent by the U.S. Geological Survey and the Federal Emergency Management Agency flood insurance claims, as well as a satellite-based flood map, the National Water Model (NWM), and the Fathom (LISFLOOD-FP) model simulated flood map. The proposed hydrologic and hydraulic model simulation indicated that it could capture 87% of all flood insurance claims within the study area, and the overall error of water depth was 0.91 m, which is comparable to the mainstream operational flood models (NWM and Fathom).

Open access
Matthew D. Cann and Allen B. White

Abstract

Nonbrightband (NBB) rain is a shallow, orographic precipitation that does not produce a radar bright band as a result of melting ice crystals. However, NBB rain is not the same as warm rain, which excludes ice from being involved in the microphysical growth of precipitation. Despite this difference, NBB rain is often treated as warm rain in the literature, and past studies have mostly ignored the role of ice. Here, we use two wet seasons (2015/16 and 2016/17) at four precipitation-observing sites in the Northern Coast Ranges of California to show the role of echo top height and ice in determining NBB rain intensity. It was found that NBB rain was only absent of bright bands 32%–46% of the time depending on location of the site. Additionally, all NBB rain rates that exceeded 10 mm h−1 exhibited observable bright bands during the hour period. We also define growth efficiency (GE) as the ability of shallow rain clouds to produce raindrops larger than drizzle size (D > 0.5 mm). High-GE rain drop size distributions were composed of fewer small drops and more large drops than low-GE rain, which was mostly drizzle. High-GE rain occurred with echo top heights above the freezing level where rapid growth of precipitation was observed by radar. Echo tops that only extended 1 km or less above the freezing level suggested hydrometeor growth from mixed-phase processes, indicating that ice may be present in coastal precipitation at warmer temperatures than previously considered.

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Enrico Zorzetto and Laifang Li

Abstract

By modulating the moisture flux from ocean to adjacent land, the North Atlantic subtropical high (NASH) western ridge significantly influences summer-season total precipitation over the conterminous United States (CONUS). However, its influence on the frequency and intensity of daily rainfall events over the CONUS remains unclear. Here we introduce a Bayesian statistical model to investigate the impacts of the NASH western ridge position on key statistics of daily scale summer precipitation, including the intensity of rainfall events, the probability of precipitation occurrence, and the probability of extreme values. These statistical quantities play a key role in characterizing both the impact of wet extremes (e.g., the probability of floods) and dry extremes. By applying this model to historical rain gauge records (1948–2019) covering the entire CONUS, we find that the western ridge of the NASH influences the frequency of rainfall as well as the distribution of rainfall intensities over extended areas of the CONUS. In particular, we find that the NASH ridge also modulates the frequency of extreme rainfall, especially that over part of the Southeast and Upper Midwest. Our analysis underlines the importance of including the NASH western ridge position as a predictor for key statistical rainfall properties to be used for hydrological applications. This result is especially relevant for projecting future changes in daily rainfall regimes over the CONUS based on the predicted strengthening of the NASH in a warming climate.

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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

Abstract

This paper describes a new Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) developed for snow-cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (<90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (ATMS) is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust both for sensors in dry conditions (total precipitable water < 10 mm) and for mean surface elevation < 2500 m, independent of the cloud cover. The algorithm shows very good performance for cold temperatures (2-m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (>90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers.

Open access
Clara Sophie Draper

Abstract

The ensembles used in the NOAA National Centers for Environmental Prediction (NCEP) global data assimilation and numerical weather prediction (NWP) system are under-dispersed at and near the land surface, preventing their use in ensemble-based land data assimilation. Comparison to offline (land-only) data assimilation ensemble systems suggests that while the relevant atmospheric fields are under-dispersed in NCEP’s system, this alone cannot explain the under-dispersed land component, and an additional scheme is required to explicitly account for land model error. This study then investigates several schemes for perturbing the soil (moisture and temperature) states in NCEP’s system, qualitatively comparing the induced ensemble spread to independent estimates of the forecast error standard deviation in soil moisture, soil temperature, 2m temperature, and 2m humidity. Directly adding perturbations to the soil states, as is commonly done in offline systems, generated unrealistic spatial patterns in the soil moisture ensemble spread. Application of a Stochastically Perturbed Physics Tendencies scheme to the soil states is inherently limited in the amount of soil moisture spread that it can induce. Perturbing the land model parameters, in this case vegetation fraction, generated a realistic distribution in the ensemble spread, while also inducing perturbations in the land (soil states) and atmosphere (2m states) that are consistent with errors in the land/atmosphere fluxes. The parameter perturbation method is then recommended for NCEP’s ensemble system, and it is currently being refined within the development of an ensemble-based coupled land/atmosphere data assimilation for NCEP’s NWP system.

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Omar V. Müller, Pier Luigi Vidale, Benoît Vannière, Reinhard Schiemann, and Patrick C. McGuire

Abstract

Previous studies showed that high-resolution GCMs overestimate land precipitation when compared against observation-based data. Particularly, high-resolution HadGEM3-GC3.1 shows a significant precipitation increase in mountainous regions, where the scarcity of gauge stations increases the uncertainty of gridded observations and reanalyses. This work evaluates such precipitation uncertainties indirectly through the assessment of river discharge, considering that an increase of ~10% in land precipitation produces ~28% more runoff when the resolution is enhanced from 1° to 0.25°, and ~50% of the global runoff is produced in 27% of global land dominated by mountains. We diagnosed the river flow by routing the runoff generated by HadGEM3-GC3.1 low- and high-resolution simulations. The river flow is evaluated using a set of 344 monitored catchments distributed around the world. We also infer the global discharge by constraining the simulations with observations following a novel approach that implies bias correction in monitored rivers with two methods, and extension of the correction to the river mouth, and along the coast. Our global discharge estimate is 47.4±1.6×103 km 3 yr −1, which is closer to the original high-resolution estimate (50.5 × 103 km 3 yr −1) than to the low-resolution (39.6 × 103 km 3 yr −1). The assessment suggests that high-resolution simulations performbetter in mountainous regions, either because the better-defined orography favours the placement of precipitation in the correct catchment, leading to a more accurate distribution of runoff, or the orographic precipitation increases, reducing the dry runoff bias of coarse resolution simulations. However, high-resolution slightly increases wet biases in catchments dominated by flat terrain. The improvement of model parameterizations and tuning may reduce the remaining errors in high-resolution simulations.

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Sanjib Sharma, Michael Gomez, Klaus Keller, Robert Nicholas, and Alfonso Mejia

Abstract

Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies.

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Nergui Nanding, Huan Wu, Jing Tao, Viviana Maggioni, Hylke E. Beck, Naijun Zhou, Maoyi Huang, and Zhijun Huang

Abstract

This study characterizes precipitation error propagation through a distributed hydrological model based on the river basins across the Contiguous United States (CONUS), to better understand the relationship between errors in precipitation inputs and simulated discharge (i.e., P-Q error relationship). The NLDAS-2 precipitation and its simulated discharge are used as the reference to compare with TMPA-3B42 V7, TMPA-3B42RT V7, StageIV, CPC-U, MERRA-2, and MSWEP-2.2 for 1,548 well gauged river basins. The relative errors in multiple conventional precipitation products and their corresponding discharges are analysed for the period of 2002-2013. The results reveal positive linear P-Q error relationships at annual and monthly timescales, and the stronger linearity for larger temporal accumulations. Precipitation errors can be doubled in simulated annual accumulated discharge. Moreover, precipitation errors are strongly dampened in basins characterized by temperate and continental climate regimes, particularly for peak discharges, showing highly nonlinear relationships. Radar-based precipitation product consistently shows dampening effects on error propagation through discharge simulations at different accumulation timescales compared to the other precipitation products. Although basin size and topography also influence the P-Q error relationship and propagation of precipitation errors, their roles depend largely on precipitation products, seasons and climate regimes.

Open access