skip to main content


Title: Incorporation of Satellite Precipitation Uncertainty in a Landslide Hazard Nowcasting System
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.  more » « less
Award ID(s):
1928724
PAR ID:
10225976
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of Hydrometeorology
Volume:
21
Issue:
8
ISSN:
1525-755X
Page Range / eLocation ID:
1741 to 1759
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatiotemporal scale. We propose a spectral error model that accounts for the neighboring space–time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency–wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space–time linear filtering term. The random errors are represented as a nonstationary additive noise. The spectral error model is applied to the IMERG multisatellite precipitation product, and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge–radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass (attenuating the fine-scale variability). While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from different sources shall not be expected to automatically have statistically independent errors.

    Significance Statement

    Satellite precipitation products are nowadays widely used for climate and environmental research, water management, risk analysis, and decision support at the local, regional, and global scales. For all these applications, knowledge about the accuracy of the products is critical for their usability. However, products are not systematically provided with a quantitative measure of the uncertainty associated with each individual estimate. Various parametric error models have been proposed for uncertainty quantification, mostly assuming that the uncertainty is only a function of the precipitation intensity at the pixel and time of interest. By projecting satellite precipitation fields and their retrieval errors into the Fourier frequency–wavenumber domain, we show that we can explicitly take into account the neighboring space–time multiscale dynamics of precipitation and compute a scale-dependent uncertainty.

     
    more » « less
  2. null (Ed.)
    When forest conditions are mapped from empirical models, uncertainty in remotely sensed predictor variables can cause the systematic overestimation of low values, underestimation of high values, and suppression of variability. This regression dilution or attenuation bias is a well-recognized problem in remote sensing applications, with few practical solutions. Attenuation is of particular concern for applications that are responsive to prediction patterns at the high end of observed data ranges, where systematic error is typically greatest. We addressed attenuation bias in models of tree species relative abundance (percent of total aboveground live biomass) based on multitemporal Landsat and topoclimatic predictor data. We developed a multi-objective support vector regression (MOSVR) algorithm that simultaneously minimizes total prediction error and systematic error caused by attenuation bias. Applied to 13 tree species in the Acadian Forest Region of the northeastern U.S., MOSVR performed well compared to other prediction methods including single-objective SVR (SOSVR) minimizing total error, Random Forest (RF), gradient nearest neighbor (GNN), and Random Forest nearest neighbor (RFNN) algorithms. SOSVR and RF yielded the lowest total prediction error but produced the greatest systematic error, consistent with strong attenuation bias. Underestimation at high relative abundance caused strong deviations between predicted patterns of species dominance/codominance and those observed at field plots. In contrast, GNN and RFNN produced dominance/codominance patterns that deviated little from observed patterns, but predicted species relative abundance with lower accuracy and substantial systematic error. MOSVR produced the least systematic error for all species with total error often comparable to SOSVR or RF. Predicted patterns of dominance/codominance matched observations well, though not quite as well as GNN or RFNN. Overall, MOSVR provides an effective machine learning approach to the reduction of systematic prediction error and should be fully generalizable to other remote sensing applications and prediction problems. 
    more » « less
  3. null (Ed.)
    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. 
    more » « less
  4. Abstract

    Diffuse solar radiation is an important, but understudied, component of the Earth’s surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980–2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m−2) for diffuse radiation and −0.2 W m−2(root mean square error = 19.2 W m−2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.

     
    more » « less
  5. Abstract As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space-time features are captured for use in hydrologic modeling, climate studies and other applications. Here we propose a space-time Fourier spectral analysis and define a suite of metrics which evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space-time scales at which a satellite product reproduces the variability of a reference “ground-truth” product (“effective resolution”). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space-time structure. We then evaluate five high-resolution multi-satellite products (CMORPH, GSMaP, IMERG-early, IMERG-final and PERSIANN-CCS) over a period of two years over the southeastern US. All five satellite products show generally consistent space-time power spectral density when compared to a reference ground gauge-radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively “smooth”. The products also show low levels of spectral coherence with the gauge-radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space-time spectral coherence, the IMERG-final product shows superior ability in resolving the space-time dynamics of precipitation down to 200 km and 4 h scales compared to the other products. 
    more » « less