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Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds' life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations. [ABSTRACT FROM AUTHOR]

In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develop the Geostationary Operational Environmental Satellites (GOES)-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a product of hourly fire perimeters, active-fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019 to 2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters using 12 h data from the Visible Infrared Imaging Radiometer Suite (VIIRS)-derived Fire Event Data Suite (FEDS) and final fire perimeters from the California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has a coarser resolution (2 km at the Equator), the final fire perimeters from GOFER correspond reasonably well to those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP; the IoU indicates the area of overlap over the area of the union relative to the reference perimeters, in which 0 is no agreement and 1 is perfect agreement. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery, where perimeters are available at intervals of 12 h or longer or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 kmh-1. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in the rates of fire spread on diurnal timescales. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates (10.5281/zenodo.8327264, Liu et al., 2023). [ABSTRACT FROM AUTHOR]

Understanding the nature and origin of errors in satellite precipitation products is important for applications and product improvement. Here we propose a new error decomposition scheme incorporating precipitation event (continuous rainy periods) information to characterize satellite errors. Under this framework, the errors are attributed to the inaccuracies in event occurrence, timing (event start/end time), and intensity. The Integrated MultisatellitE Retrieval for Global Precipitation Measurement (IMERG) is used as our test product to apply the method over CONUS. The above‐listed factors contribute approximately 30%, 20%, and 50% to the total bias, respectively. Significant asymmetry exists in the temporal distribution of biases throughout events: early event endings cause threefold more precipitation amount bias than late event beginnings, while early event beginnings cause fourfold more bias than late event endings. Dominant contributors vary across seasons and regions. The proposed error decomposition provides insight into sources of error for improved retrievals. Plain Language Summary: Satellite remote sensing offers unique capabilities to map global precipitation at daily to sub‐daily scales, important for hydrologic applications and decision‐making. However, inherent uncertainties and errors in satellite precipitation products necessitate a comprehensive understanding of their characteristics and sources for effective utilization and enhancement. Here, we propose a new event‐based error decomposition scheme to characterize satellite errors. This approach is based on the understanding that precipitation occurs as individual events (continuous rainy periods); hence, any quantitative inaccuracy in a satellite product can be attributed to the imperfect delineation of diverse event facets: (a) occurrence (completely missed/falsely detected events), (b) timing (wrong start/end times of the detected events), and (c) intensity (inaccurate precipitation rates during the events). We apply the method to a popular high‐resolution satellite product, the Integrated MultisatellitE Retrieval for Global Precipitation Measurement (IMERG) over CONUS. Results show that, nationwide, the above three error types contribute on average about 30%, 20%, and 50% to the total bias, respectively. A large fraction of errors is associated with events starting/ending too early in the satellite product. Dominant error types are season‐ and region‐dependent. The event‐based error breakdown offers potential to diagnose error sources and guide algorithm improvement for satellite precipitation products. Key Points: A new event‐based error decomposition scheme for satellite precipitation products is proposed, dividing the total bias into 10 componentsInaccuracies in event occurrence, timing, and intensity contribute on average to about 30%, 20%, and 50% of the total amount biasA large fraction of errors is associated with events starting/ending too early in the satellite product [ABSTRACT FROM AUTHOR]

In the western United States, prolonged drought, warming climate, and historical fuel build-up have contributed to larger and more intense wildfires, as well as longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heatwaves, windstorms, droughts, and varying levels of active fire suppression, influence fire spread. Here we develop the GOES-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a dataset of hourly fire perimeters, active fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019-2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters with using 12-hourly data from the VIIRS-derived Fire Event Data Suite (FEDS) and final fire perimeters from California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has coarser resolution (2 km at the equator), the final fire perimeters from GOFER correspond reasonably well with those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP. GOFER fills a key temporal gap present in other fire tracking products that rely on low-earth- orbit imagery, where perimeters are available at 12-hour intervals or longer, or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km/h. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in rates of fire spread on diurnal time scales. The resulting GOFER dataset has a broad set of applications, including the development of predictive models for fire spread and improvement of atmospheric transport models for surface smoke estimates. [ABSTRACT FROM AUTHOR]

Predicting sediment yield from recently burned areas remains a challenge but is important for hazard and resource management as wildfire impacts increase. Here we use lidar‐based monitoring of two fires in southern California, USA to study the movement of sediment during pre‐rainfall periods and postfire periods of flooding and debris flows over multiple storm events. Using a data‐driven approach, we examine the relative importance of terrain, vegetation, burn severity, and rainfall amounts through time on sediment yield. We show that incipient fire‐activated dry sediment loading and pre‐fire colluvium were rapidly flushed out by debris flows and floods but continued erosion occurred later in the season from soil erosion and, in ∼9% of catchments, from shallow landslides. Based on these observations, we develop random forest regression models to predict dry ravel and incipient runoff‐driven sediment yield applicable to small steep headwater catchments in southern California. Plain Language Summary: Wildfire makes watersheds more susceptible to hazardous flash flooding and debris flows, yet characterization and prediction of these hazards remains limited. In this study, we used repeat airborne laser mapping to quantify the movement of sediment in steep burn areas during initial dry periods and subsequent erosion from runoff events. Based on these observations, we developed two predictive models: one to predict the filling of channels with sediment prior to rainfall and a second one to predict erosion by debris flows and floods during initial storm events, which showed improvement over another commonly used model. After initial runoff events, much of the available sediment in channels was transported downstream, however small landslides and extensive erosion of soils across the landscape continued to supply sediment to floods and debris flows, in line with studies elsewhere showing continued debris flow activity despite reduced sediment in channels. Our study demonstrates that airborne laser mapping together with data‐driven modeling offer opportunities to increase predictive ability of post‐fire erosion and such approaches should be further explored in regions such as northern California, where fire is expanding and models of post‐fire erosion need to be tested and refined. Key Points: Postfire dry and wet sediment transport were quantified with lidar and dominant controls were identified using random forest regressionSlope and sediment supply, including dry ravel, were the strongest controls on initial sediment yield by debris flows and floodsContinued sediment bulking occurred from soil erosion and patchy mass wasting later in the wet season as channels became supply limited [ABSTRACT FROM AUTHOR]

The use of deep-learning algorithms for estimating the value of geophysical variables from remotely sensed information is rapidly expanding. The typical objective function minimized in such algorithms is the mean square error (MSE), which is known to lead to smooth estimates with compressed dynamical range as compared to the true distribution of the variable of interest. Here, we introduce and evaluate alternative objective functions, focusing on the retrieval of precipitation rates from satellite passive microwave radiometric measurements using a deep convolutional neural network. For this testbed application, the results show that explicitly imposing the preservation of the statistical distribution and spatial wavelet power spectrum of the target variable allows to accurately reproduce extreme values and sharp gradients across multiple scales. [ABSTRACT FROM AUTHOR]

Predicting the transport of bedload tracer particles is a problem of significant theoretical and practical interest. Yet, little understanding exists for transport in rivers in the presence of bedforms, which may trap grains and thereby influence travel distance. In a series of flume experiments with a sandy gravel bed in a large experimental flume, bed elevation and tracer travel distances were measured at high resolution for a range of discharges. As discharge increased, bedform height increased and bedform length decreased, increasing bedform steepness. For all tracer sizes and flow conditions, bedforms act as primary controls on the tracer travel distances. Bedform trapping increases linearly with the ratio of bedform height to tracer grain size, with 50% trapping efficiency for a ratio of two and 90% trapping efficiency for a ratio of four. A theoretical model based on the extended active layer formulation for sediment transport is able to capture much of the distribution of measured travel distances for all tracer sizes and discharges, providing a first connection between tracer transport theory and bedform trapping and indicating normal diffusion of tracers at relatively small timescales. Variable bedform geometry can influence trap efficiency for individual bedforms and the theoretical model can help identify "preferential trapping" conditions. The distribution of tracer travel distances for a mixture of grain sizes and variable discharge, as expected in natural rivers, displays heavy tail characteristics. Key Points: Bedforms serve as a primary control on tracer travel distancesBedform trap efficiency depends linearly on the relative bedform heightTravel distances of a mixture of particles and under variable discharge exhibit a heavy‐tailed distribution [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]