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Niko Wanders
,
Stephan Thober
,
Rohini Kumar
,
Ming Pan
,
Justin Sheffield
,
Luis Samaniego
, and
Eric F. Wood

Abstract

Hydrological forecasts with a high temporal and spatial resolution are required to provide the level of information needed by end users. So far high-resolution multimodel seasonal hydrological forecasts have been unavailable due to 1) lack of availability of high-resolution meteorological seasonal forecasts, requiring temporal and spatial downscaling; 2) a mismatch between the provided seasonal forecast information and the user needs; and 3) lack of consistency between the hydrological model outputs to generate multimodel seasonal hydrological forecasts. As part of the End-to-End Demonstrator for Improved Decision Making in the Water Sector in Europe (EDgE) project commissioned by the Copernicus Climate Change Service (ECMWF), this study provides a unique dataset of seasonal hydrological forecasts derived from four general circulation models [CanCM4, GFDL Forecast-Oriented Low Ocean Resolution version of CM2.5 (GFDL-FLOR), ECMWF Season Forecast System 4 (ECMWF-S4), and Météo-France LFPW] in combination with four hydrological models [mesoscale hydrologic model (mHM), Noah-MP, PCRaster Global Water Balance (PCR-GLOBWB), and VIC]. The forecasts are provided at daily resolution, 6-month lead time, and 5-km spatial resolution over the historical period from 1993 to 2012. Consistency in hydrological model parameterization ensures an increased consistency in the hydrological forecasts. Results show that skillful discharge forecasts can be made throughout Europe up to 3 months in advance, with predictability up to 6 months for northern Europe resulting from the improved predictability of the spring snowmelt. The new system provides an unprecedented ensemble of seasonal hydrological forecasts with significant skill over Europe to support water management. This study highlights the potential advantages of multimodel based forecasting system in providing skillful hydrological forecasts.

Open access
Ming Pan
,
Eric F. Wood
,
Dennis B. McLaughlin
,
Dara Entekhabi
, and
Lifeng Luo

Abstract

The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a hydrologic data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A synthetic data assimilation experiment is performed, driven by meteorological forcing fields downscaled from the ensemble forecasts made by the NOAA/National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The classic full-rank ensemble Kalman filter is used as the benchmark to evaluate the multiscale filter performance, and comparisons are also made with a horizontally uncoupled filter. It is demonstrated that the multiscale filter is able to closely approximate the full-rank solution with a low computational cost (∼1/20 of the full-rank filter) in an experiment in which the top-layer soil moisture is assimilated, whereas the horizontally uncoupled filter fails to approximate the full-rank solution.

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A. A. M. Holtslag
,
E. I. F. De Bruijn
, and
H-L. Pan

Abstract

This paper describes a high resolution air mass transformation (AMT) model. The model is intended for short-range weather forecasts of the temperature and humidity profiles in the lower atmosphere, the structure of the boundary layer, the boundary layer height, and the amount of boundary layer clouds. The AMT model consists of a one-dimensional, multilayer boundary layer model, which is advected along trajectories from a source region to a receptor point. The trajectories are calculated within a larger scale (limited area) model. The initial profiles for temperature and humidity are obtained from observed radiosondes. The paper describes the physical and dynamical background of the model. With the model we have made case studies of the development of stratocumulus over the North Sea, and have simulated the representation of clear skies over land. The output of the model is compared with the output of the ECMWF model and the current operational bulk AMT model. Sensitivity of the model to boundary and initial conditions is discussed. In addition to the case studies the model has been used as an operational forecast tool in 77 cases. These cases have been evaluated by independent forecasters. Since the model performed well and because no large computing facilities are needed, it is concluded that the model is a useful tool for the short-range weather forecaster.

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Hylke E. Beck
,
Eric F. Wood
,
Ming Pan
,
Colby K. Fisher
,
Diego G. Miralles
,
Albert I. J. M. van Dijk
,
Tim R. McVicar
, and
Robert F. Adler

Abstract

We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.

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Ming Pan
,
Alok K. Sahoo
,
Tara J. Troy
,
Raghuveer K. Vinukollu
,
Justin Sheffield
, and
Eric F. Wood

Abstract

A systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984–2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems.

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Yuan Yang
,
Ming Pan
,
Peirong Lin
,
Hylke E. Beck
,
Zhenzhong Zeng
,
Dai Yamazaki
,
Cédric H. David
,
Hui Lu
,
Kun Yang
,
Yang Hong
, and
Eric F. Wood

Abstract

Better understanding and quantification of river floods for very local and “flashy” events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high-resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-yr period of 1980–2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-yr return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) subdaily modeling. This data record, referred as Global Reach-Level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.

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Laura L. Pan
,
Kenneth P. Bowman
,
Elliot L. Atlas
,
Steve C. Wofsy
,
Fuqing Zhang
,
James F. Bresch
,
Brian A. Ridley
,
Jasna V. Pittman
,
Cameron R. Homeyer
,
Pavel Romashkin
, and
William A. Cooper

The Stratosphere–Troposphere Analyses of Regional Transport 2008 (START08) experiment investigated a number of important processes in the extratropical upper troposphere and lower stratosphere (UTLS) using the National Science Foundation (NSF)–NCAR Gulfstream V (GV) research aircraft. The main objective was to examine the chemical structure of the extratropical UTLS in relation to dynamical processes spanning a range of scales. The campaign was conducted during April–June 2008 from Broomfield, Colorado. A total of 18 research flights sampled an extensive geographical region of North America (25°–65°N, 80°–120°W) and a wide range of meteorological conditions. The airborne in situ instruments measured a comprehensive suite of chemical constituents and microphysical variables from the boundary layer to the lower stratosphere, with flights specifically designed to target key transport processes in the extratropical UTLS. The flights successfully investigated stratosphere–troposphere exchange (STE) processes, including the intrusion of tropospheric air into the stratosphere in association with the secondary tropopause and the intrusion of stratospheric air deep into the troposphere. The flights also sampled the influence of convective transport and lightning on the upper troposphere as well as the distribution of gravity waves associated with multiple sources, including fronts and topography. The aircraft observations are complemented by satellite observations and modeling. The measurements will be used to improve the representation of UTLS chemical gradients and transport in Chemistry–Climate models (CCMs). This article provides an overview of the experiment design and selected observational highlights.

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Jason C. Knievel
,
Yubao Liu
,
Thomas M. Hopson
,
Justin S. Shaw
,
Scott F. Halvorson
,
Henry H. Fisher
,
Gregory Roux
,
Rong-Shyang Sheu
,
Linlin Pan
,
Wanli Wu
,
Joshua P. Hacker
,
Erik Vernon
,
Frank W. Gallagher III
, and
John C. Pace

Abstract

Since 2007, meteorologists of the U.S. Army Test and Evaluation Command (ATEC) at Dugway Proving Ground (DPG), Utah, have relied on a mesoscale ensemble prediction system (EPS) known as the Ensemble Four-Dimensional Weather System (E-4DWX). This article describes E-4DWX and the innovative way in which it is calibrated, how it performs, why it was developed, and how meteorologists at DPG use it. E-4DWX has 30 operational members, each configured to produce forecasts of 48 h every 6 h on a 272-processor high performance computer (HPC) at DPG. The ensemble’s members differ from one another in initial-, lateral-, and lower-boundary conditions; in methods of data assimilation; and in physical parameterizations. The predictive core of all members is the Advanced Research core of the Weather Research and Forecasting (WRF) Model. Numerical predictions of the most useful near-surface variables are dynamically calibrated through algorithms that combine logistic regression and quantile regression, generating statistically realistic probabilistic depictions of the atmosphere’s future state at DPG’s observing sites. Army meteorologists view E-4DWX’s output via customized figures posted to a restricted website. Some of these figures summarize collective results—for example, through means, standard deviations, or fractions of the ensemble exceeding thresholds. Other figures show each forecast, individually or grouped—for example, through spaghetti diagrams and time series. This article presents examples of each type of figure.

Open access
Luis Samaniego
,
Stephan Thober
,
Niko Wanders
,
Ming Pan
,
Oldrich Rakovec
,
Justin Sheffield
,
Eric F. Wood
,
Christel Prudhomme
,
Gwyn Rees
,
Helen Houghton-Carr
,
Matthew Fry
,
Katie Smith
,
Glenn Watts
,
Hege Hisdal
,
Teodoro Estrela
,
Carlo Buontempo
,
Andreas Marx
, and
Rohini Kumar

Abstract

Simulations of water fluxes at high spatial resolution that consistently cover historical observations, seasonal forecasts, and future climate projections are key to providing climate services aimed at supporting operational and strategic planning, and developing mitigation and adaptation policies. The End-to-end Demonstrator for improved decision-making in the water sector in Europe (EDgE) is a proof-of-concept project funded by the Copernicus Climate Change Service program that addresses these requirements by combining a multimodel ensemble of state-of-the-art climate model outputs and hydrological models to deliver sectoral climate impact indicators (SCIIs) codesigned with private and public water sector stakeholders from three contrasting European countries. The final product of EDgE is a water-oriented information system implemented through a web application. Here, we present the underlying structure of the EDgE modeling chain, which is composed of four phases: 1) climate data processing, 2) hydrological modeling, 3) stakeholder codesign and SCII estimation, and 4) uncertainty and skill assessments. Daily temperature and precipitation from observational datasets, four climate models for seasonal forecasts, and five climate models under two emission scenarios are consistently downscaled to 5-km spatial resolution to ensure locally relevant simulations based on four hydrological models. The consistency of the hydrological models is guaranteed by using identical input data for land surface parameterizations. The multimodel outputs are composed of 65 years of historical observations, a 19-yr ensemble of seasonal hindcasts, and a century-long ensemble of climate impact projections. These unique, high-resolution hydroclimatic simulations and SCIIs provide an unprecedented information system for decision-making over Europe and can serve as a template for water-related climate services in other regions.

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THE TERRAIN-INDUCED ROTOR EXPERIMENT

A Field Campaign Overview Including Observational Highlights

Vanda Grubišić
,
James D. Doyle
,
Joachim Kuettner
,
Stephen Mobbs
,
Ronald B. Smith
,
C. David Whiteman
,
Richard Dirks
,
Stanley Czyzyk
,
Stephen A. Cohn
,
Simon Vosper
,
Martin Weissmann
,
Samuel Haimov
,
Stephan F. J. De Wekker
,
Laura L. Pan
, and
Fotini Katopodes Chow

The Terrain-Induced Rotor Experiment (T-REX) is a coordinated international project, composed of an observational field campaign and a research program, focused on the investigation of atmospheric rotors and closely related phenomena in complex terrain. The T-REX field campaign took place during March and April 2006 in the lee of the southern Sierra Nevada in eastern California. Atmospheric rotors have been traditionally defined as quasi-two-dimensional atmospheric vortices that form parallel to and downwind of a mountain ridge under conditions conducive to the generation of large-amplitude mountain waves. Intermittency, high levels of turbulence, and complex small-scale internal structure characterize rotors, which are known hazards to general aviation. The objective of the T-REX field campaign was to provide an unprecedented comprehensive set of in situ and remotely sensed meteorological observations from the ground to UTLS altitudes for the documentation of the spatiotemporal characteristics and internal structure of a tightly coupled system consisting of an atmospheric rotor, terrain-induced internal gravity waves, and a complex terrain boundary layer. In addition, T-REX had several ancillary objectives including the studies of UTLS chemical distribution in the presence of mountain waves and complex-terrain boundary layer in the absence of waves and rotors. This overview provides a background of the project including the information on its science objectives, experimental design, and observational systems, along with highlights of key observations obtained during the field campaign.

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