Quantifying Errors in TRMM-Based Multi-Sensor QPE Products Over Land in Preparation for GPM

Determining uncertainties in satellite-based multi-sensor quantitative precipitation estimates over land of fundamental importance to both data producers and hydro climatological applications. ,Evaluating TRMM-era products also lays the groundwork and sets the direction for algorithm and applications development for future missions including GPM. QPE uncertainties result mostly from the interplay of systematic errors and random errors. In this work, we will synthesize our recent results quantifying the error characteristics of satellite-based precipitation estimates. Both systematic errors and total uncertainties have been analyzed for six different TRMM-era precipitation products (3B42, 3B42RT, CMORPH, PERSIANN, NRL and GSMap). For systematic errors, we devised an error decomposition scheme to separate errors in precipitation estimates into three independent components, hit biases, missed precipitation and false precipitation. This decomposition scheme reveals hydroclimatologically-relevant error features and provides a better link to the error sources than conventional analysis, because in the latter these error components tend to cancel one another when aggregated or averaged in space or time. For the random errors, we calculated the measurement spread from the ensemble of these six quasi-independent products, and thus produced a global map of measurement uncertainties. The map yields a global view of the error characteristics and their regional and seasonal variations, reveals many undocumented error features over areas with no validation data available, and provides better guidance to global assimilation of satellite-based precipitation data. Insights gained from these results and how they could help with GPM will be highlighted

Performance of IMERG as a Function of Spatiotemporal Scale

The Integrated Multi-satellitE Retrievals for GPM (IMERG), a global high resolution gridded precipitation data set, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scale and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final run of IMERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1 deg to 2.5 deg) and accumulation periods (from 0.5 h to 24 h) over a region in the southeastern US. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge based operational precipitation dataset, is used. The TRMM Multisatellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies

The Impact of AMSR-E Soil Moisture Assimilation on Evapotranspiration Estimation

An assessment ofETestimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) soil moisture products into the Noah LSM Version 3.2 with the North American LDAS phase 2 CNLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product

A Lagrangian Analysis of Cold Cloud Clusters and Their Life Cycles With Satellite Observations

Cloud movement and evolution signify the complex water and energy transport in the atmosphere-ocean-land system. Detecting, clustering, and tracking clouds as semi coherent cluster objects enables study of their evolution which can complement climate model simulations and enhance satellite retrieval algorithms, where there are large gaps between overpasses. Using an area-overlap cluster tracking algorithm, in this study we examine the trajectories, horizontal extent, and brightness temperature variations of millions of individual cloud clusters over their lifespan, from infrared satellite observations at 30-minute, 4-km resolution, for a period of 11 years. We found that the majority of cold clouds were both small and short-lived and that their frequency and location are influenced by El Nino. More importantly, this large sample of individually tracked clouds shows their horizontal size and temperature evolution. Longer lived clusters tended to achieve their temperature and size maturity milestones at different times, while these stages often occurred simultaneously in shorter lived clusters. On average, clusters with this lag also exhibited a greater rainfall contribution than those where minimum temperature and maximum size stages occurred simultaneously. Furthermore, by examining the diurnal cycle of cluster development over Africa and the Indian subcontinent, we observed differences in the local timing of the maximum occurrence at different life cycle stages. Over land there was a strong diurnal peak in the afternoon while over the ocean there was a semi-diurnal peak composed of longer-lived clusters in the early morning hours and shorter-lived clusters in the afternoon. Building on regional specific work, this study provides a long-term, high-resolution, and global survey of object-based cloud characteristics

Land Surface Modeling and Data Assimilation to Support Physical Precipitation Retrievals for GPM

Objective: The objective of this proposal is to provide a routine land surface modeling and data assimilation capability for GPM in order to provide global land surface states that are necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in GPM, is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. Therefore, providing a robust capability to routinely provide these critical land states is essential to support GPM-era physical retrieval algorithms over land

Surface urban heat island effect and its spatiotemporal dynamics in metropolitan area: a case study in the Zhengzhou metropolitan area, China

The deterioration of the urban surface thermal environment has seriously affected regional environments and human health, becoming a critical ecological problem faced by cities worldwide. This study focused on surface urban heat island effect in metropolitan area and selected the emerging metropolitan area of Zhengzhou, China, as a case study. Based on the MODIS land surface temperature data obtained from the Google Earth Engine the surface urban heat island intensity (SUHII) was calculated and its temporal and spatial dynamics were analyzed from 2003 to 2022. The main findings indicated that Zhengzhou, the core city of the metropolitan area, had the strongest urban heat island effect with day surface urban heat island intensity of 1.10°C and night SUHII of 1.39°C). Generally, the average annual SUHII was higher during the day than at night, and the maximum value was detected in summer (2.43°C). SUHII showed an increasing trend at night, especially in summer during the study period. It decreased obviously in urban centers during the day, while it increased obviously in the outer urban areas at night. The results of this study contributed to the understanding of the spatiotemporal dynamics of the urban heat island effect in the Zhengzhou metropolitan area

Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model

A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50 deg. N - 50 deg. S at relatively high spatial (approximately 12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is approximately 0.9 and the false alarm ratio is approximately 0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30 deg. S - 30 deg. N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input

Solitary beam propagation in a nonlinear optical resonator enables high-efficiency pulse compression and mode self-cleaning

Generating intense ultrashort pulses with high-quality spatial modes is crucial for ultrafast and strong-field science. This can be accomplished by controlling propagation of femtosecond pulses under the influence of Kerr nonlinearity and achieving stable propagation with high intensity. In this work, we propose that the generation of spatial solitons in periodic layered Kerr media can provide an optimum condition for supercontinuum generation and pulse compression using multiple thin plates. With both the experimental and theoretical investigations, we successfully identify these solitary modes and reveal a universal relationship between the beam size and the critical nonlinear phase. Space-time coupling is shown to strongly influence the spectral, spatial and temporal profiles of femtosecond pulses. Taking advantage of the unique characters of these solitary modes, we demonstrate single-stage supercontinuum generation and compression of femtosecond pulses from initially 170 fs down to 22 fs with an efficiency ~90%. We also provide evidence of efficient mode self-cleaning which suggests rich spatial-temporal self-organization processes of laser beams in a nonlinear resonator