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Ethan D. Gutmann, Joseph. J. Hamman, Martyn P. Clark, Trude Eidhammer, Andrew W. Wood, and Jeffrey R. Arnold

Abstract

Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

Open access
Hongli Liu, Andrew W. Wood, Andrew J. Newman, and Martyn P. Clark

Abstract

Most datasets of surface meteorology are deterministic, yet many applications using these datasets require or can benefit from uncertainty estimates in meteorological fields. Motivated by this gap, we evaluated the use of a spatial regression method to estimate the uncertainty in precipitation and temperature fields of existing deterministic gridded meteorological datasets. Taking the widely used North American Land Data Assimilation System 2 (NLDAS-2) precipitation and temperature dataset as an example, we used the deterministic NLDAS-2 values to generate ensemble estimates for daily precipitation, mean temperature, and the diurnal temperature range. Our method is a form of ensemble dressing. Nine variations were tested to assess the impacts of sampling density on the estimates of the mean and uncertainty, and one strategy was selected to generate 100 ensemble members at 1/8° and daily resolution for the period 1979–2019, termed as the Ensemble Dressing of NLDAS-2 (EDN2). Compared with an independent station-based ensemble dataset, the ensemble dressing method produces reasonable uncertainty patterns for precipitation and underestimates uncertainty for temperature. For precipitation, the uncertainty increases with the increase in daily accumulation. For temperature, the uncertainty is relatively small in the warm season and large in the cold season. This ensemble dressing method is applicable to other deterministic gridded meteorological datasets. The generated spatiotemporally varying uncertainty information could support applications such as land surface and hydrologic modeling, data assimilation, and forecasting, especially where application models are tied to a specific meteorological dataset.

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Zhenchen Liu, Wen Zhou, Ruhua Zhang, Yue Zhang, and Ya Wang

Abstract

Droughts and associated near-surface temperature anomalies can be attributed to amplified vertical subsidence and anomalous anticyclonic circulations from dynamic perspectives. However, two open and interesting issues remain unknown: 1) whether hydrometeorological situations under droughts can be reproduced directly utilizing variability of atmospheric dynamics and 2) what specific roles atmospheric dynamics play in drought reconstruction. To explore these questions, this study employs three kinds of dynamic features (i.e., vertical velocity, relative vorticity, and horizontal divergence) for hydrometeorological reconstruction (e.g., precipitation and near-surface air temperature) under drought situations through a so-called XGBoost (extreme gradient boosting) ensemble learning method. The study adopts two different reconstruction schemes (i.e., statistically preexisting dynamic–hydrometeorological relationships and interannual variability) and finds dynamically based reconstruction feasible. The three main achievements are as follows. 1) Regarding different hydrometeorological situations reconstructed with preexisting dynamic–hydrometeorological relationships, good reconstruction performance can be captured with the same or different lead times, depending on whether the evolution of dynamic anomalies (e.g., vertical motion and relative vorticity) is temporally asynchronous. 2) Reconstruction on the interannual scale performs relatively well, seemingly regardless of seasonality and drought-inducing mechanisms. 3) More importantly, from interpretable perspectives, global-scale analysis of dynamic contributions helps discover unexpected dynamic drought-inducing roles and associated latitudinal modulation. That is, low-level cyclonic/anticyclonic anomalies contribute to drought development in the northern middle and high latitudes, while upper-level vertical subsidence contributes significantly to tropical near-surface temperature anomalies concurrent with droughts. These achievements could provide guidance for dynamically based drought monitoring and prediction in different geographic regions.

Significance Statement

It is common sense that severe drought events are physically attributable to amplified vertical subsidence and anomalous anticyclonic circulations. However, the specific contributions of atmospheric dynamics, together with the feasibility of dynamically based drought reconstruction, are crucial components that are seldom investigated. To our knowledge, this manuscript is the first to reconstruct drought utilizing atmospheric dynamics and to interpret quantified dynamic contributions; it also represents a new interdisciplinary attempt to reproduce hydrological variability based on routine atmospheric dynamic variables.

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Fadji Z. Maina, Sujay V. Kumar, Ishrat Jahan Dollan, and Viviana Maggioni

Abstract

Precipitation estimates are highly uncertain in complex regions such as High Mountain Asia (HMA), where ground measurements are very difficult to obtain and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA’s strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets [the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and the ECMWF reanalysis ERA5] with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatiotemporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

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Carlos M. Carrillo, Colin P. Evans, Brian N. Belcher, and Toby R. Ault

Abstract

We investigated the predictability (forecast skill) of short-term droughts using the Palmer drought severity index (PDSI). We incorporated a sophisticated data training (of decadal range) to evaluate the improvement of forecast skill of short-term droughts (3 months). We investigated whether the data training of the synthetic North American Multi-Model Ensemble (NMME) climate has some influence on enhancing short-term drought predictability. The central elements are the merged information among PDSI and NMME with two postprocessing techniques. 1) The bias correction–spatial disaggregation (BC-SD) method improves spatial resolution by using a refined soil information introduced in the available water capacity of the PDSI calculation to assess water deficit that better estimates drought variability. 2) The ensemble model output statistic (EMOS) approach includes systematically trained decadal information of the multimodel ensemble simulations. Skill of drought forecasting improves when using EMOS, but BC-SD does not increase the forecast skill when compared with an analysis using BC (low spatial resolution). This study suggests that predictability forecast of drought (PDSI) can be extended without any change in the core dynamics of the model but instead by using the sophisticated EMOS postprocessing technique. We pointed out that using NMME without any postprocessing is of limited use in the suite of model variations of the NMME, at least for the U.S. Northeast. From our analysis, 1 month is the most extended range we should expect, which is below the range of the seasonal scale presented with EMOS (2 months). Thus, we propose a new design of drought forecasts that explicitly includes the multimodel ensemble signal.

Open access
Clement Guilloteau, Efi Foufoula-Georgiou, Pierre Kirstetter, Jackson Tan, and George J. Huffman

Abstract

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.

Open access
Mohammadvaghef Ghazvinian, Yu Zhang, Thomas M. Hamill, Dong-Jun Seo, and Nelun Fernando

Abstract

Conventional statistical postprocessing techniques offer limited ability to improve the skills of probabilistic guidance for heavy precipitation. This paper introduces two artificial neural network (ANN)-based, geographically aware, and computationally efficient postprocessing schemes, namely, the ANN-multiclass (ANN-Mclass) and the ANN–censored, shifted gamma distribution (ANN-CSGD). Both schemes are implemented to postprocess Global Ensemble Forecast System (GEFS) forecasts to produce probabilistic quantitative precipitation forecasts (PQPFs) over the contiguous United States (CONUS) using a short (60 days), rolling training window. The performances of these schemes are assessed through a set of hindcast experiments, wherein postprocessed 24-h PQPFs from the two ANN schemes were compared against those produced using the benchmark quantile mapping algorithm for lead times ranging from 1 to 8 days. Outcomes of the hindcast experiments show that ANN schemes overall outperform the benchmark as well as the raw forecast over the CONUS in predicting probability of precipitation over a range of thresholds. The relative performance varies among geographic regions, with the two ANN schemes broadly improving upon quantile mapping over the central, south, and southeast, and slightly underperforming along the Pacific coast where skills of raw forecasts are the highest. Between the two schemes, the hybrid ANN-CSGD outperforms at higher rainfall thresholds (i.e., >50 mm day−1), though the outperformance comes at a slight expense of sharpness and spatial specificity. Collectively, these results confirm the ability of the ANN algorithms to produce skillful PQPFs with a limited training window and point to the prowess of the hybrid scheme for calibrating PQPFs for rare-to-extreme rainfall events.

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E. D. Rappin, R. Mahmood, U. S. Nair, and R. A. Pielke Sr.

Abstract

This paper analyzed observations from the Great Plains Irrigation Experiment (GRAINEX) to better understand land–atmosphere (L–A) interactions and PBL evolution. This study is focused on a day when the largest forcing on the boundary layer originated from the land surface/land use. To examine these impacts, we also applied the Weather Research and Forecasting (WRF) Model. Results from the observations show that compared to nonirrigated areas, air temperature, wind speed, and PBL height (PBLH) were lower while dewpoint temperature and latent heat flux were higher over irrigated areas. Findings suggest that entrainment layer drying and differences in energy partitioning over irrigated and nonirrigated areas played an important role in PBL evolution. In the final hours of the day, the PBL collapsed faster over nonirrigated areas compared to irrigated. The WRF Model simulations agree with these observations. They also show that the extent of irrigation [expressed as irrigation fraction (IF)] in an area impacts L–A response. Under ∼60% IF, the latent heat flux and mixing ratio reach their highest value while temperature and PBLH are at their lowest, and sensible heat flux is near its lowest value. Results are reversed for ∼2% IF. It is concluded that irrigation notably impacts L–A interactions and PBL evolution.

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Jinghua Xiong, Shenglian Guo, Abhishek, Jun Li, and Jiabo Yin

Abstract

Multiple indicators derived from the Gravity Recovery and Climate Experiment (GRACE) satellite have been used in monitoring floods and droughts. However, these measures are constrained by the relatively short time span (∼20 years) and coarse temporal resolution (1 month) of the GRACE and GRACE Follow-On missions, and the inherent decay mechanism of the land surface system has not been considered. Here we reconstructed the daily GRACE-like terrestrial water storage anomaly (TWSA) in the Yangtze River basin (YRB) during 1961–2015 based on the Institute of Geodesy at Graz University of Technology (ITSG)-Grace2018 solution using the random forest (RF) model. A novel antecedent metric, namely, standardized drought and flood potential index (SDFPI), was developed using reconstructed TWSA, observed precipitation, and modeled evapotranspiration. The potential of SDFPI was evaluated against in situ discharge, VIC simulations, and several widely used indices such as total storage deficit index (TSDI), self-calibrated Palmer drought severity index (sc-PDSI), and multiscale standardized precipitation evapotranspiration index (SPEI). Daily SDFPI was utilized to monitor and characterize short-term severe floods and droughts. The results illustrate a reasonably good accuracy of ITSG-Grace2018 solution when compared with the hydrological model output and regional water balance estimates. The RF model presents satisfactory performances for the TWSA reconstruction, with a correlation coefficient of 0.88 and Nash–Sutcliffe efficiency of 0.76 during the test period 2011–15. Spatiotemporal propagation of the developed SDFPI corresponds well with multiple indices when examined for two typical short-term events, including the 2003 flood and 2013 drought. A total of 22 submonthly exceptional floods and droughts were successfully detected and featured using SDFPI, highlighting its outperformance and capabilities in providing inferences for decision-makers and stakeholders to monitor and mitigate the short-term floods and droughts.

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Zhixia Wang, Shengzhi Huang, Qiang Huang, Weili Duan, Guoyong Leng, Yi Guo, Xudong Zheng, Mingqiu Nie, Zhiming Han, Haixia Dong, and Jian Peng

Abstract

In the propagation from meteorological to hydrological drought, there are time-lag and step-abrupt effects, quantified in terms of propagation time and threshold, which play an important role in hydrological drought early warning. However, seasonal drought propagation time and threshold and their dynamics as well as the corresponding driving mechanism remain unknown in a changing environment. To this end, the standardized precipitation index (SPI) and standardized runoff index (SRI) were used respectively to characterize meteorological and hydrological droughts and to determine the optimal propagation time. Then, a seasonal drought propagation framework based on Bayesian network was proposed for calculating the drought propagation threshold with SPI. Finally, the seasonal dynamics and preliminary attribution of propagation characteristics were investigated based on the random forest model and correlation analysis. The results show that 1) relatively short propagation time (less than 9 months) and large propagation threshold (from −3.18 to −1.19) can be observed in the Toxkan River basins (subbasin II), especially for spring, showing low drought resistance; 2) drought propagation time shows an extended trend in most seasons, while the drought propagation threshold displays an increasing trend in autumn and winter in the Aksu River basin (subbasins I–II), and the opposite characteristics in the Hotan and Yarkant River basins (subbasins III–V); and 3) the impacts of precipitation, temperature, potential evapotranspiration, and soil moisture on drought propagation dynamics are inconsistent across subbasins and seasons, noting that reservoirs serve as a buffer to regulate the propagation from meteorological to hydrological droughts. The findings of this study can provide scientific guidelines for watershed hydrological drought early warning and risk management.

Significance Statement

The aim of this study is to better understand how the delayed and step-abrupt effects of propagation from meteorological drought to hydrological drought can be characterized through propagation time and threshold. These response indicators determine the resistance of a catchment to hydrological droughts and meteorological droughts. They can help water resources management agencies to mitigate hydrological droughts by taking measures such as water storage, increasing revenue, and reducing expenditure. The findings of this study can provide scientific guidelines for watershed hydrological drought early warning and risk management.

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