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Gab Abramowitz
,
Andy Pitman
,
Hoshin Gupta
,
Eva Kowalczyk
, and
Yingping Wang

Abstract

A neural network–based flux correction technique is applied to three land surface models. It is then used to show that the nature of systematic model error in simulations of latent heat, sensible heat, and the net ecosystem exchange of CO2 is shared between different vegetation types and indeed different models. By manipulating the relationship between the dataset used to train the correction technique and that used to test it, it is shown that as much as 45% of per-time-step model root-mean-square error in these flux outputs is due to systematic problems in those model processes insensitive to changes in vegetation parameters. This is shown in the three land surface models using flux tower measurements from 13 sites spanning 2 vegetation types. These results suggest that efforts to improve the representation of fundamental processes in land surface models, rather than parameter optimization, are the key to the development of land surface model ability.

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Terri S. Hogue
,
Soroosh Sorooshian
,
Hoshin Gupta
,
Andrea Holz
, and
Dean Braatz

Abstract

Operational flood forecasting models vary in complexity, but nearly all have parameters for which values must be estimated. The traditional and widespread manual calibration approach requires considerable training and experience and is typically laborious and time consuming. Under the Advanced Hydrologic Prediction System modernization program, National Weather Service (NWS) hydrologists must produce rapid calibrations for roughly 4000 forecast points throughout the United States. The classical single-objective automatic calibration approach, although fast and objective, has not received widespread acceptance among operational hydrologists. In the work reported here, University of Arizona researchers and NWS personnel have collaborated to combine the strengths of the manual and automatic calibration strategies. The result is a multistep automatic calibration scheme (MACS) that emulates the progression of steps followed by NWS hydrologists during manual calibration and rapidly provides acceptable parameter estimates. The MACS approach was tested on six operational basins (drainage areas from 671 to 1302 km2) in the North Central River Forecast Center (NCRFC) area. The results were found to compare favorably with the NCRFC manual calibrations in terms of both visual inspection and statistical measures, such as daily root-mean-square error and percent bias by flow group. Further, implementation of the MACS procedure requires only about 3–4 person hours per basin, in contrast to the 15–20 person hours typically required using the manual approach. Based on this study, the NCRFC has opted to perform further testing of the MACS procedure at a large number of forecast points that constitute the Grand River (Michigan) forecast group. MACS is a time-saving, reliable approach that can provide calibrations that are of comparable quality to the NCRFC’s current methods.

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Jasper A. Vrugt
,
Hoshin V. Gupta
,
BreanndánÓ Nualláin
, and
Willem Bouten

Abstract

Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River, Mississippi, indicate that forecast performance improvements on the order of 30% to 50% can be realized even with a suboptimal implementation of the filter. Further, the SODA parameter estimates appear to be less biased, which may increase the prospects for finding useful regionalization relationships.

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Yuqiong Liu
,
Luis A. Bastidas
,
Hoshin V. Gupta
, and
Soroosh Sorooshian

Abstract

Surface water and energy balance plays an important role in land surface models, especially in coupled land surface–atmospheric models due to the complicated interactions between land surfaces and the overlying atmosphere. The primary purpose of this paper is to demonstrate the significant negative impacts that a minor deficiency in the parameterization of canopy evaporation may have on offline and coupled land surface model simulations. In this research, using the offline NCAR Land Surface Model (LSM) and the locally coupled NCAR Single-column Community Climate Model (SCCM) as examples, intensive effort has been focused on the exploration of the mechanisms involved in the activation of unrealistically high canopy evaporation and thus unreasonable surface energy partitions because of a minor deficiency in the parameterization of canopy evaporation. The main causes responsible for exacerbating the impacts of the deficiency of the land surface model through the coupling of the two components are analyzed, along with possible impacts of land surface parameters in triggering the problems. Results from experimental runs show that, for a large number of randomly generated physically realistic land surface parameter sets, this model deficiency has caused the occurrences of negative canopy water with a significantly high frequency for both the offline NCAR LSM and the coupled NCAR SCCM, suggesting that land surface parameters are not the only important factors in triggering the problems associated with the model deficiency. In addition, the concurrence of intense solar radiation and enough precipitation is identified to be mainly responsible for exacerbating the negative impacts of the parameterization deficiency. Finally, a simple adjustment has been made in this study to effectively prevent the occurrences of negative canopy water storages, leading to significantly improved model performances.

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Liming Xu
,
Soroosh Sorooshian
,
Xiaogang Gao
, and
Hoshin V. Gupta

Abstract

A new cloud-patch method for the identification and removal of no-rain cold clouds from infrared (IR) imagery is presented. A cloud patch is defined as a cluster of connected IR imagery pixels that are colder than a given IR brightness temperature threshold. The threshold is derived through a combination of the rainfall field estimated from microwave observations and the IR data closely coincident with microwave sensor satellite overpasses. Seven cloud-patch features are used to describe cloud-top properties, including six IR based and one VIS based. The ID3 algorithm is used to extract structural knowledge from a training dataset and to produce classification rules expressed explicitly on the values of various patch features; these rules can be used to explain the physical principles underlying the cloud classification. The method was evaluated for the Japanese islands and surrounding oceans using AIP/1 data for June (training period) and July–August (evaluation period) 1989. The results of identifying no-rain cloud patches are very good for both periods in spite of the change in rainfall regime from frontal to subtropical convective. Nearly 20% of the total pixels and 60% of the no-rain cloud pixels were removed with negligible rain losses due to misclassification. Moreover, visible data were found to be useful for enhancing the no-rain cold patch identification and thereby reducing the rain loss.

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Zhao Yang
,
Francina Dominguez
,
Hoshin Gupta
,
Xubin Zeng
, and
Laura Norman

Abstract

Land-use and land-cover change (LULCC) due to urban expansion alter the surface albedo, heat capacity, and thermal conductivity of the surface. Consequently, the energy balance in urban regions is different from that of natural surfaces. To evaluate the changes in regional climate that could arise because of projected urbanization in the Phoenix–Tucson corridor, Arizona, this study applied the coupled WRF Model–Noah–Urban Canopy Model (UCM; which includes a detailed urban radiation scheme) to this region. Land-cover changes were represented using land-cover data for 2005 and projections to 2050, and historical North American Regional Reanalysis (NARR) data were used to specify the lateral boundary conditions. Results suggest that temperature changes will be well defined, reflecting the urban heat island (UHI) effect within areas experiencing LULCC. Changes in precipitation are less robust but seem to indicate reductions in precipitation over the mountainous regions northeast of Phoenix and decreased evening precipitation over the newly urbanized area.

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Kou-lin Hsu
,
Xiaogang Gao
,
Soroosh Sorooshian
, and
Hoshin V. Gupta

Abstract

A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. The model was initially calibrated over the Japanese Islands using remotely sensed infrared data collected by the Geostationary Meteorological Satellite (GMS) and ground-based data collected by the Automated Meteorological Data Acquisition System (AMeDAS). The model was then validated for both the Japanese Islands (using GMS and AMeDAS data) and the Florida peninsula (using GOES-8 and NEXRAD data). An adaptive procedure is used to recursively update the network parameters when ground-based data are available. This feature dramatically improves the estimation performance in response to the diverse precipitation characteristics of different geographical regions and time of year. The model can also be successfully updated using only spatially and/or temporally limited observation data such as ground-based rainfall measurements. Another important feature is a procedure that provides insights into the functional relationships between the input variables and output rainfall rate.

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Yuqiong Liu
,
Hoshin V. Gupta
,
Soroosh Sorooshian
,
Luis A. Bastidas
, and
William J. Shuttleworth

Abstract

In coupled land surface–atmosphere modeling, the possibility and benefits of constraining model parameters using observational data bear investigation. Using the locally coupled NCAR Single-column Community Climate Model (NCAR SCCM), this study demonstrates some feasible, effective approaches to constrain parameter estimates for coupled land–atmosphere models and explores the effects of including both land surface and atmospheric parameters and fluxes/variables in the parameter estimation process, as well as the value of conducting the process in a stepwise manner. The results indicate that the use of both land surface and atmospheric flux variables to construct error criteria can lead to better-constrained parameter sets. The model with “optimal” parameters generally performs better than when a priori parameters are used, especially when some atmospheric parameters are included in the parameter estimation process. The overall conclusion is that, to achieve balanced, reasonable model performance on all variables, it is desirable to optimize both land surface and atmospheric parameters and use both land surface and atmospheric fluxes/variables for error criteria in the optimization process. The results also show that, for a coupled land–atmosphere model, there are potential advantages to using a stepwise procedure in which the land surface parameters are first identified in offline mode, after which the atmospheric parameters are determined in coupled mode. This stepwise scheme appears to provide comparable solutions to a fully coupled approach, but with considerably reduced computational time. The trade-off in the ability of a model to satisfactorily simulate different processes simultaneously, as observed in most multicriteria studies, is most evident for sensible heat and precipitation in this study for the NCAR SCCM.

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Terri S. Hogue
,
Luis Bastidas
,
Hoshin Gupta
,
Soroosh Sorooshian
,
Ken Mitchell
, and
William Emmerich

Abstract

This paper investigates the performance of the National Centers for Environmental Prediction (NCEP) Noah land surface model at two semiarid sites in southern Arizona. The goal is to evaluate the transferability of calibrated parameters (i.e., direct application of a parameter set to a “similar” site) between the sites and to analyze model performance under the various climatic conditions that can occur in this region. A multicriteria, systematic evaluation scheme is developed to meet these goals. Results indicate that the Noah model is able to simulate sensible heat, ground heat, and ground temperature observations with a high degree of accuracy, using the optimized parameter sets. However, there is a large influx of moist air into Arizona during the monsoon period, and significant latent heat flux errors are observed in model simulations during these periods. The use of proxy site parameters (transferred parameter set), as well as traditional default parameters, results in diminished model performance when compared to a set of parameters calibrated specifically to the flux sites. Also, using a parameter set obtained from a longer-time-frame calibration (i.e., a 4-yr period) results in decreased model performance during nonstationary, short-term climatic events, such as a monsoon or El Niño. Although these results are specific to the sites in Arizona, it is hypothesized that these results may hold true for other case studies. In general, there is still the opportunity for improvement in the representation of physical processes in land surface models for semiarid regions. The hope is that rigorous model evaluation, such as that put forth in this analysis, and studies such as the Project for the Intercomparison of Land-Surface Processes (PILPS) San Pedro–Sevilleta, will lead to advances in model development, as well as parameter estimation and transferability, for use in long-term climate and regional environmental studies.

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Gab Abramowitz
,
Hoshin Gupta
,
Andy Pitman
,
Yingping Wang
,
Ray Leuning
,
Helen Cleugh
, and
Kuo-lin Hsu

Abstract

Data assimilation in the field of predictive land surface modeling is generally limited to using observational data to estimate optimal model states or restrict model parameter ranges. To date, very little work has attempted to systematically define and quantify error resulting from a model's inherent inability to simulate the natural system. This paper introduces a data assimilation technique that moves toward this goal by accounting for those deficiencies in the model itself that lead to systematic errors in model output. This is done using a supervised artificial neural network to “learn” and simulate systematic trends in the model output error. These simulations in turn are used to correct the model's output each time step. The technique is applied in two case studies, using fluxes of latent heat flux at one site and net ecosystem exchange (NEE) of carbon dioxide at another. Root-mean-square error (rmse) in latent heat flux per time step was reduced from 27.5 to 18.6 W m−2 (32%) and monthly from 9.91 to 3.08 W m−2 (68%). For NEE, rmse per time step was reduced from 3.71 to 2.70 μmol m−2 s−1 (27%) and annually from 2.24 to 0.11 μmol m−2 s−1 (95%). In both cases the correction provided significantly greater gains than single criteria parameter estimation on the same flux.

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