Xiaogang Gao

Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

Abstract A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (Tb–R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and Tb–R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfal...

Multimodel Combination Techniques for Analysis of Hydrological Simulations: Application to Distributed Model Intercomparison Project Results

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the bestcalibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction.

A Microwave Infrared Threshold Technique to Improve the GOES Precipitation Index

Abstract A method to improve the GOES Precipitation Index (GPI) technique by combining satellite microwave and infrared (IR) data is proposed and tested. Using microwave-based rainfall estimates, the method, termed the Universally Adjusted GPI (UAGPI), modifies both GPI parameters (i.e., the IR brightness temperature threshold and the mean rain rate) to minimize summation of estimation errors during the microwave sampling periods. With respect to each grid, monthly rainfall estimates are obtained in a manner identical to the GPI except for the use of the optimized parameters. The proposed method is compared with the Adjusted GPI (AGPI) method of Adler et al. (1993), which adjusts the GPI monthly rainfall estimates directly using an adjustment ratio. The two methods are compared using the First Algorithm Intercomparison Project (AIP/1) dataset, which covers two month-long periods over the Japanese islands and surrounding oceanic regions. Two types of microwave-related errors are addressed during the compar...

Estimating Rainfall Intensities from Weather Radar Data: The Scale-Dependency Problem

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities ( R) are calculated from the observed radar reflectivities ( Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z‐R transformation equation. The scale dependency of the power-law Z‐R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative ( a) and exponent (b) parameters are said to be ‘‘scale dependent’’ if applying the observed and calculated rainfall intensities to objective function at different scale results in different ‘‘optimal’’ parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z‐R relationship determination requires further knowledge of reflectivity and rainintensity error structure.

A new evolutionary search strategy for global optimization of high-dimensional problems

Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle populations capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones.

Handling boundary constraints for particle swarm optimization in high-dimensional search space

Despite the fact that the popular particle swarm optimizer (PSO) is currently being extensively applied to many real-world problems that often have high-dimensional and complex fitness landscapes, the effects of boundary constraints on PSO have not attracted adequate attention in the literature. However, in accordance with the theoretical analysis in [11], our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high-dimensional search spaces. Consequently, the method used to handle boundary violations is critical to the performance of PSO. In this study, we reveal that the widely used random and absorbing bound-handling schemes may paralyze PSO for high-dimensional and complex problems. We also explore in detail the distinct mechanisms responsible for the failures of these two bound-handling schemes. Finally, we suggest that using high-dimensional and complex benchmark functions, such as the composition functions in [19], is a prerequisite to identifying the potential problems in applying PSO to many real-world applications because certain properties of standard benchmark functions make problems inexplicit.

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Author(s): Yang, T; Asanjan, AA; Welles, E; Gao, X; Sorooshian, S; Liu, X | Abstract:

Simulating California reservoir operation using the classification and regression‐tree algorithm combined with a shuffled cross‐validation scheme

Author(s): Yang, T; Gao, X; Sorooshian, S; Li, X | Abstract:

Improved representation of diurnal variability of rainfall retrieved from the Tropical Rainfall Measurement Mission Microwave Imager adjusted Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) system

[1] Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) is a satellite infrared-based algorithm that produces global estimates of rainfall at resolutions of 0.25° x 0.25° and a half-hour. In this study the model parameters of PERSIANN are routinely adjusted using coincident rainfall derived from the Tropical Rainfall Measurement Mission Microwave Imager (TMI). The impact of such an adjustment on capturing the diurnal variability of rainfall is examined for the Boreal summer of 2002. General evaluations of the PERSIANN rainfall estimates with/without TMI adjustment were conducted using U.S. daily gauge rainfall and nationwide radar network (weather surveillance radar) 1988 Doppler data. The diurnal variability of PERSIANN rainfall estimates with TMI adjustment is improved over those without TMI adjustment. In particular, the amounts of afternoon and morning maximums in rainfall diurnal cycles improved by 14.9% and 26%, respectively, and the original 2-3 hours of time lag in the phase of diurnal cycles improved by 1-2 hours. In addition, the rainfall estimate with TMI adjustment has higher correlation (0.75 versus 0.63) and reduced bias (+8% versus -11%) at monthly 0.25° x 0.25° resolution than that without TMI adjustment and consistently shows higher correlation (0.62 versus 0.51) and lower bias (+22% versus -30%) at daily 0.25° x 0.25° scale. This study provides evidence that the TMI, which measures instantaneous rain rates from the TRMM platform flying on a non-Sun-synchronous orbit, enables PERSIANN to capture more realistic diurnal variations of rainfall. This study also reveals the limitation of current satellite rainfall estimation techniques in retrieving the rainfall diurnal features and suggests that further investigation of precipitation generation in different periods of cloud life cycles might help resolve this limitation.

Computational Earth Science: Big Data Transformed Into Insight

More than ever in the history of science, researchers have at their fingertips an unprecedented wealth of data from continuously orbiting satellites, weather monitoring instruments, ecological observatories, seismic stations, moored buoys, floats, and even model simulations and forecasts. With just an internet connection, scientists and engineers can access atmospheric and oceanic gridded data and time series observations, seismographs from around the world, minute-by-minute conditions of the near-Earth space environment, and other data streams that provide information on events across local, regional, and global scales. These data sets have become essential for monitoring and understanding the associated impacts of geological and environmental phenomena on society.