Search Results

You are looking at 1 - 10 of 15 items for

  • Author or Editor: Dalia Kirschbaum x
  • Refine by Access: All Content x
Clear All Modify Search
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 early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for the Global Precipitation Mission (IMERG), provides NRT 0.1° and 30-minute precipitation estimates across the globe with only a 4-hour latency. This study was an evaluation of the GPM IMERG version 6 level-3 Early Run 30-minute 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-minute precipitation from gauges and 30-minute precipitation from the IMERG multi-satellite constellation. False positive and false negative precipitation events were identified and analyzed to determine 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.

Restricted access
Daniel B. Wright
,
Dalia B. Kirschbaum
, and
Soni Yatheendradas

Abstract

Satellite multisensor precipitation products (SMPPs) have a variety of potential uses but suffer from relatively poor accuracy due to systematic biases and random errors in precipitation occurrence and magnitude. The censored, shifted gamma distribution (CSGD) is used here to characterize the Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis (TMPA), a commonly used SMPP, and to compare it against the rain gauge–based North American Land Data Assimilation System phase 2 (NLDAS-2) reference precipitation dataset across the conterminous United States. The CSGD describes both the occurrence and the magnitude of precipitation. Climatological CSGD characterization reveals significant regional differences between TMPA and NLDAS-2 in terms of magnitude and probability of occurrence. A flexible CSGD-based error modeling framework is also used to quantify errors in TMPA relative to NLDAS-2. The framework can model conditional bias as either a linear or nonlinear function of satellite precipitation rate and can produce a “conditional CSGD” describing the distribution of “true” precipitation based on a satellite observation. The framework is also used to “merge” TMPA with atmospheric variables from version 2 of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) to reduce SMPP errors. Despite the coarse resolution of MERRA-2, this merging offers robust reductions in random error due to the better performance of numerical models in resolving stratiform precipitation. Improvements in the near-real-time version of TMPA are relatively greater than for the higher-latency research version.

Full access
Dalia Kirschbaum
,
Robert Adler
,
David Adler
,
Christa Peters-Lidard
, and
George Huffman

Abstract

It is well known that extreme or prolonged rainfall is the dominant trigger of landslides worldwide. While research has evaluated the spatiotemporal distribution of extreme rainfall and landslides at local or regional scales using in situ data, few studies have mapped rainfall-triggered landslide distribution globally because of the dearth of landslide data and consistent precipitation information. This study uses a newly developed global landslide catalog (GLC) and a 13-yr satellite-based precipitation record from Tropical Rainfall Measuring Mission (TRMM) data. For the first time, these two unique products provide the foundation to quantitatively evaluate the co-occurrence of precipitation and rainfall-triggered landslides globally. Evaluation of the GLC indicates that 2010 had a large number of high-impact landslide events relative to previous years. This study considers how variations in extreme and prolonged satellite-based rainfall are related to the distribution of landslides over the same time scales for three active landslide areas: Central America, the Himalayan arc, and central eastern China. Several test statistics confirm that TRMM rainfall generally scales with the observed increase in landslide reports and fatal events for 2010 and previous years over each region. These findings suggest that the co-occurrence of satellite precipitation and landslide reports may serve as a valuable indicator for characterizing the spatiotemporal distribution of landslide-prone areas in order to establish a global rainfall-triggered landslide climatology. This study characterizes the variability of satellite precipitation data and reported landslide activity at the global scale in order to improve landslide cataloging and attempt to quantify landslide triggering at daily, monthly, and yearly time scales.

Full access
Andrew M. Badger
,
Christa Peters-Lidard
, and
Dalia B. Kirschbaum

Abstract

A globally consistent ground validation method for remotely sensed precipitation products is crucial for building confidence in these products. This study develops a new methodology to validate the IMERG precipitation products through the use of SMAP soil moisture changes as a proxy for precipitation occurrence. Using a standard 2 × 2 contingency table method, preliminary results provide confidence in SMAP’s ability to be utilized as a validation tool for IMERG as results are comparable to previous validation studies. However, the method allows for an overestimate of false alarm frequency due to light precipitation events that can evaporate before the subsequent SMAP overpass and changes in overpass-to-overpass SMAP soil moisture that are within the range of SMAP uncertainty. To counter these issues, a 3 × 3 contingency table is used to reduce noise and extract more signal from the detection method. Through the use of this novel approach, the validation method produces a global mean POD of 0.64 and global mean FAR of 0.40, the first global-scale ground validation skill scores for the IMERG products. Advancing the method to validate precipitation quantity and the development of a real-time validation for the IMERG Early product are the crucial next developments.

Significance Statement

We wanted to see if there was a method in which remotely sensed precipitation observations could be validated at a near-global scale for land areas. Scientific literature is filled with studies that validate various precipitation datasets over local-to-regional scales, with very few extending beyond that domain. This study provides a robust first attempt at validating a global precipitation product at a global scale using changes in remotely sensed soil moisture as an independent proxy for precipitation presence/absence. While the method demonstrates that there is skill in using soil moisture as a tool to validate precipitation at the global scale, we find that there are still instances of a systemic bias for arid climate regimes. This method lays the groundwork for future studies to provide a comprehensive global validation in a globally consistent manner.

Full access
Thomas Stanley
,
Dalia B. Kirschbaum
,
George J. Huffman
, and
Robert F. Adler

Abstract

Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.

Full access
Jessica V. Fayne
,
Aakash Ahamed
,
Justin Roberts-Pierel
,
Amanda C. Rumsey
, and
Dalia Kirschbaum

Abstract

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open-source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat-8 Operational Land Imager sensor, elevation data from the Shuttle Radar Topography Mission, and precipitation data from the Global Precipitation Measurement mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change-detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-Time Increased Precipitation (DRIP) model that helps to identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state of the art for landslide detection. A case study and validation exercise in Nepal were performed for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium-resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool.

Full access
Sujay V. Kumar
,
Kenneth W. Harrison
,
Christa D. Peters-Lidard
,
Joseph A. Santanello Jr.
, and
Dalia Kirschbaum

Abstract

Observing system simulation experiments (OSSEs) are often conducted to evaluate the worth of existing data and data yet to be collected from proposed new missions. As missions increasingly require a broader “Earth systems” focus, it is important that the OSSEs capture the potential benefits of the observations on end-use applications. Toward this end, the results from the OSSEs must also be evaluated with a suite of metrics that capture the value, uncertainty, and information content of the observations while factoring in both science and societal impacts. This article presents a soil moisture OSSE that employs simulated L-band measurements and assesses its utility toward improving drought and flood risk estimates using the NASA Land Information System (LIS). A decision-theory-based analysis is conducted to assess the economic utility of the observations toward improving these applications. The results suggest that the improvements in surface soil moisture, root-zone soil moisture, and total runoff fields obtained through the assimilation of L-band measurements are effective in providing improvements in the drought and flood risk assessments as well. The decision-theory analysis not only demonstrates the economic utility of observations but also shows that the use of probabilistic information from the model simulations is more beneficial compared to the use of corresponding deterministic estimates. The experiment also demonstrates the value of a comprehensive modeling environment such as LIS for conducting end-to-end OSSEs by linking satellite observations, physical models, data assimilation algorithms, and end-use application models in a single integrated framework.

Full 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
Zhe Li
,
Daniel B. Wright
,
Sara Q. Zhang
,
Dalia B. Kirschbaum
, and
Samantha H. Hartke

Abstract

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations.

Full access
Paul A. Kucera
,
Elizabeth E. Ebert
,
F. Joseph Turk
,
Vincenzo Levizzani
,
Dalia Kirschbaum
,
Francisco J. Tapiador
,
Alexander Loew
, and
M. Borsche

Advances to space-based observing systems and data processing techniques have made precipitation datasets quickly and easily available via various data portals and widely used in Earth sciences. The increasingly lengthy time span of space-based precipitation data records has enabled cross-discipline investigations and applications that would otherwise not be possible, revealing discoveries related to hydrological and land processes, climate, atmospheric composition, and ocean freshwater budget and proving a vital element in addressing societal issues. The purpose of this article is to demonstrate how the availability and continuity of precipitation data records from recent and upcoming space missions is transforming the ways that scientific and societal issues are addressed, in ways that would not be otherwise possible.

Full access