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  • Author or Editor: Thomas Stanley x
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Jessica R. P. Sutton
,
Dalia Kirschbaum
,
Thomas Stanley
, and
Elijah Orland

Abstract

Accurately detecting and estimating precipitation at near–real time (NRT) is of utmost importance for the early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), provides NRT 0.1° and 30-min precipitation estimates across the globe with only a 4-h latency. This study was an evaluation of the GPM IMERG version 6 level-3 early run 30-min precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-min precipitation from gauges and 30-min precipitation from the IMERG multisatellite constellation. False-positive and false-negative precipitation events were identified and analyzed to determine their characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months, suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged passive microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG–estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards.

Significance Statement

Accurately estimating rainfall to detect and monitor a precipitation event at near–real time is of utmost importance for hydrometeorological hazards. We used a state-of-the-art rainfall estimation product called GPM IMERG that uses infrared and passive microwave measurements collected from a constellation of satellites to produce near-real-time rainfall estimates every 30 min worldwide. The purpose of our research was to identify when, where, and why GPM IMERG falsely detected and missed precipitation events. Our results suggest that the frequency and timing of passive microwave precipitation with forward propagation were responsible for IMERG missing events, overestimating total rainfall during some precipitation events, and underestimating total rainfall in other precipitation events. Our future work will further investigate precipitation events using the GPM IMERG version 7 near-real-time product.

Restricted access
Samantha H. Hartke
,
Daniel B. Wright
,
Dalia B. Kirschbaum
,
Thomas A. Stanley
, and
Zhe Li

Abstract

Many existing models that predict landslide hazards utilize ground-based sources of precipitation data. In locations where ground-based precipitation observations are limited (i.e., a vast majority of the globe), or for landslide hazard models that assess regional or global domains, satellite multisensor precipitation products offer a promising near-real-time alternative to ground-based data. NASA’s global Landslide Hazard Assessment for Situational Awareness (LHASA) model uses the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) product to issue hazard “nowcasts” in near–real time for areas that are currently at risk for landsliding. Satellite-based precipitation estimates, however, can contain considerable systematic bias and random error, especially over mountainous terrain and during extreme rainfall events. This study combines a precipitation error modeling framework with a probabilistic adaptation of LHASA. Compared with the routine version of LHASA, this probabilistic version correctly predicts more of the observed landslides in the study region with fewer false alarms by high hazard nowcasts. This study demonstrates that improvements in landslide hazard prediction can be achieved regardless of whether the IMERG error model is trained using abundant ground-based precipitation observations or using far fewer and more scattered observations, suggesting that the approach is viable in data-limited regions. Results emphasize the importance of accounting for both random error and systematic satellite precipitation bias. The approach provides an example of how environmental prediction models can incorporate satellite precipitation uncertainty. Other applications such as flood and drought monitoring and forecasting could likely benefit from consideration of precipitation uncertainty.

Free access
John M. Forsythe
,
Jason B. Dodson
,
Philip T. Partain
,
Stanley Q. Kidder
, and
Thomas H. Vonder Haar

Abstract

The NOAA operational total precipitable water (TPW) anomaly product is available to forecasters to display percentage of normal TPW in real time for applications like heavy precipitation forecasts. In this work, the TPW anomaly is compared to multilayer cloud frequency and vertical structure. The hypothesis is tested that the TPW anomaly is reflective of changes in cloud vertical distribution, and that anomalously moist atmospheres have more and deeper clouds, while dry atmospheres have fewer and thinner clouds. Cloud vertical occurrence profiles from the CloudSat 94-GHz radar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) are composited according to TPW anomaly for summer and winter from 2007 to 2010. Three geographic regions are examined: the North Pacific (NPAC), the tropical east Pacific (Niño), and the Mississippi Valley (MSVL), which is a land-only region. Cloud likelihood increases as TPW anomaly values increase beyond 100% over MSVL and Niño. Over NPAC, shallow boundary layer cloud occurrence is not a function of TPW anomaly, while high clouds and deep clouds throughout the troposphere are more likely at higher TPW anomalies. In the Niño region, boundary layer clouds grow vertically as the TPW anomaly increases, and the anomaly range is smaller than in the midlatitudes. In summer, the MSVL region resembles Niño, but boundary layer clouds are observed less frequently than expected. The wintertime MSVL results do not show any compelling relationship, perhaps because of the difficulties in computing TPW anomaly in a very dry atmosphere.

Full access
Anne Felsberg
,
Gabriëlle J. M. De Lannoy
,
Manuela Girotto
,
Jean Poesen
,
Rolf H. Reichle
, and
Thomas Stanley

Abstract

This global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

Open access
Elise Monsieurs
,
Dalia Bach Kirschbaum
,
Jackson Tan
,
Jean-Claude Maki Mateso
,
Liesbet Jacobs
,
Pierre-Denis Plisnier
,
Wim Thiery
,
Augusta Umutoni
,
Didace Musoni
,
Toussaint Mugaruka Bibentyo
,
Gloire Bamulezi Ganza
,
Guy Ilombe Mawe
,
Luc Bagalwa
,
Clairia Kankurize
,
Caroline Michellier
,
Thomas Stanley
,
Francois Kervyn
,
Matthieu Kervyn
,
Alain Demoulin
, and
Olivier Dewitte

Abstract

Accurate precipitation data are fundamental for understanding and mitigating the disastrous effects of many natural hazards in mountainous areas. Floods and landslides, in particular, are potentially deadly events that can be mitigated with advanced warning, but accurate forecasts require timely estimation of precipitation, which is problematic in regions such as tropical Africa with limited gauge measurements. Satellite rainfall estimates (SREs) are of great value in such areas, but rigorous validation is required to identify the uncertainties linked to SREs for hazard applications. This paper presents results of an unprecedented record of gauge data in the western branch of the East African Rift, with temporal resolutions ranging from 30 min to 24 h and records from 1998 to 2018. These data were used to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research version and near-real-time products for 3-hourly, daily, and monthly rainfall accumulations, over multiple spatial scales. Results indicate that there are at least two factors that led to the underestimation of TMPA at the regional level: complex topography and high rainfall intensities. The TMPA near-real-time product shows overall stronger rainfall underestimations but lower absolute errors and a better performance at higher rainfall intensities compared to the research version. We found area-averaged TMPA rainfall estimates relatively more suitable in order to move toward regional hazard assessment, compared to data from scarcely distributed gauges with limited representativeness in the context of high rainfall variability.

Full access