scholarly journals Prediction Skill of the 2012 U.S. Great Plains Flash Drought in Subseasonal Experiment (SubX) Models

2020 ◽  
Vol 33 (14) ◽  
pp. 6229-6253 ◽  
Author(s):  
Anthony M. DeAngelis ◽  
Hailan Wang ◽  
Randal D. Koster ◽  
Siegfried D. Schubert ◽  
Yehui Chang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Initial Conditions ◽  
Rapid Onset ◽  
Prediction Skill ◽  
Soil Conditions ◽  
Lead Times ◽  
Drought Prediction ◽  
Atmospheric Circulation Anomalies ◽  
Soil Moisture Initialization

AbstractRapid-onset droughts, known as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict flash droughts in advance would greatly enhance our preparation for them and potentially mitigate their impacts. Here, we investigate the prediction skill of the extreme 2012 flash drought over the U.S. Great Plains at subseasonal lead times (3 weeks or more in advance) in global forecast systems participating in the Subseasonal Experiment (SubX). An additional comprehensive set of subseasonal hindcasts with NASA’s GEOS model, a SubX model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. The results show that the prediction skill of the SubX models is quite variable. While skillful predictions are restricted to within the first two forecast weeks in most models, skill is considerably better (3–4 weeks or more) for certain models and initialization dates. The enhanced prediction skill is found to originate from two robust sources: 1) accurate soil moisture initialization once dry soil conditions are established, and 2) the satisfactory representation of quasi-stationary cross-Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Evidence is provided that the importance of soil moisture initialization applies more generally to central U.S. summer flash droughts. Our results corroborate earlier findings that accurate soil moisture initialization is important for skillful subseasonal forecasts and highlight the need for additional research on the sources and predictability of drought-inducing quasi-stationary atmospheric circulation anomalies.

Sensitivity of U.S. Drought Prediction Skill to Land Initial States

2020 ◽  
Vol 21 (12) ◽  
pp. 2793-2811 ◽  
Author(s):  
Chul-Su Shin ◽  
Bohua Huang ◽  
Paul A. Dirmeyer ◽  
Subhadeep Halder ◽  
Arun Kumar
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Initial Conditions ◽  
The United States ◽  
Prediction Skill ◽  
Severe Drought ◽  
Eastern United States ◽  
Drought Prediction ◽  
Forecast Lead Time ◽  
Initial States

AbstractIn addition to remote SST forcing, realistic representation of land forcing (i.e., soil moisture) over the United States is critical for a prediction of U.S. severe drought events approximately one season in advance. Using “identical twin” experiments with different land initial conditions (ICs) in the 32-yr (1979–2010) CFSv2 reforecasts (NASA GLDAS-2 reanalysis versus NCEP CFSR), sensitivity and skill of U.S. drought predictions to land ICs are evaluated. Although there is no outstanding performer between the two sets of forecasts with different land ICs, each set shows greater skill in some regions, but their locations vary with forecast lead time and season. The 1999 case study demonstrates that although a pattern of below-normal SSTs in the Pacific in the fall and winter is realistically reproduced in both reforecasts, GLDAS-2 land initial states display a stronger east–west gradient of soil moisture, particularly drier in the eastern United States and more consistent with observations, leading to warmer surface temperature anomalies over the United States. Anomalies lasting for one season are accompanied by more persistent barotropic (warm core) anomalous high pressure over CONUS, which results in better prediction skill of this drought case up to 4 months in advance in the reforecasts with GLDAS-2 land ICs. Therefore, it is essential to minimize the uncertainty of land initial states among the current land analyses for improving U.S. drought prediction on seasonal time scales.

scholarly journals Potential Predictability of Long-Term Drought and Pluvial Conditions in the U.S. Great Plains

2008 ◽  
Vol 21 (4) ◽  
pp. 802-816 ◽  
Author(s):  
Siegfried D. Schubert ◽  
Max J. Suarez ◽  
Philip J. Pegion ◽  
Randal D. Koster ◽  
Julio T. Bacmeister
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
General Circulation ◽  
Storm Track ◽  
Warm Season ◽  
Circulation Model ◽  
Skewed Distribution ◽  
Soil Conditions ◽  
Unexpected Result ◽  
Potential Predictability

Abstract This study examines the predictability of seasonal mean Great Plains precipitation using an ensemble of century-long atmospheric general circulation model (AGCM) simulations forced with observed sea surface temperatures (SSTs). The results show that the predictability (intraensemble spread) of the precipitation response to SST forcing varies on interannual and longer time scales. In particular, this study finds that pluvial conditions are more predictable (have less intraensemble spread) than drought conditions. This rather unexpected result is examined in the context of the physical mechanisms that impact precipitation in the Great Plains. These mechanisms include El Niño–Southern Oscillation’s impact on the planetary waves and hence the Pacific storm track (primarily during the cold season), the role of Atlantic SSTs in forcing changes in the Bermuda high and low-level moisture flux into the continent (primarily during the warm season), and soil moisture feedbacks (primarily during the warm season). It is found that the changes in predictability are primarily driven by changes in the strength of the land–atmosphere coupling, such that under dry conditions a given change in soil moisture produces a larger change in evaporation and hence precipitation than the same change in soil moisture would produce under wet soil conditions. The above changes in predictability are associated with a negatively skewed distribution in the seasonal mean precipitation during the warm season—a result that is not inconsistent with the observations.

scholarly journals Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land–Atmosphere Coupling

2019 ◽  
Vol 20 (5) ◽  
pp. 793-819 ◽  
Author(s):  
Joseph A. Santanello Jr. ◽  
Patricia Lawston ◽  
Sujay Kumar ◽  
Eli Dennis
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Coupled Models ◽  
Wide Range ◽  
Soil Moisture Initialization

Abstract The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

Hyperresolution Land Surface Modeling in the Context of SMAP Cal–Val

2015 ◽  
Vol 17 (1) ◽  
pp. 345-352 ◽  
Author(s):  
Camille Garnaud ◽  
Stéphane Bélair ◽  
Aaron Berg ◽  
Tracy Rowlandson
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Initial Conditions ◽  
Surface Condition ◽  
Soil Conditions ◽  
Small Scale ◽  
In Situ Observations

Abstract This study explores the performance of Environment Canada’s Surface Prediction System (SPS) in comparison to in situ observations from the Brightwater Creek soil moisture observation network with respect to soil moisture and soil temperature. To do so, SPS is run at hyperresolution (100 m) over a small domain in southern Saskatchewan (Canada) during the summer of 2014. It is shown that with initial conditions and surface condition forcings based on observations, SPS can simulate soil moisture and soil temperature evolution over time with high accuracy (mean bias of 0.01 m3 m−3 and −0.52°C, respectively). However, the modeled spatial variability is generally much weaker than observed. This is likely related to the model’s use of uniform soil texture, the lack of small-scale orography, as well as a predefined crop growth cycle in SPS. Nonetheless, the spatial averages of simulated soil conditions over the domain are very similar to those observed, suggesting that both are representative of large-scale conditions. Thus, in the context of the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) project, this study shows that both simulated and in situ observations can be upscaled to allow future comparison with upcoming satellite data.

An assessment of the impact of initial soil conditions on drought and precipitation extremes by using a high-resolution regional climate model

2021 ◽  
Author(s):  
Juan José Rosa Cánovas ◽  
Matilde García-Valdecasas Ojeda ◽  
Patricio Yeste-Donaire ◽  
Emilio Romero-Jiménez ◽  
María Jesús Esteban-Parra ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Regional Climate ◽  
Precipitation Extremes ◽  
Weather Research And Forecasting ◽  
Soil Conditions ◽  
Forecasting Model ◽  
Initial Soil ◽  
Dry Conditions ◽  
The Impact

<p>Soil moisture (SM) is one of the fields with a relevant role in processes involving land-atmosphere interactions, especially in regions such as the Mediterranean Europe, where coupling between those components of the climate system is very strong. The aim of this study is to address the impact of initial soil conditions on drought and precipitation extremes over the Iberian Peninsula (IP). For this purpose, a dynamical downscalling experiment has been conducted by using the Weather Research and Forecasting model (WRF) along the period 1990-2000. Two one-way nested domains has been considered: a finer domain spanning the IP, with spatial resolution around 10 km, nested within a coarser domain covering the Euro-CORDEX region at 50 km of spatial resolution.</p><p>WRF simulations have been driven with ERA-Interim reanalysis data for all fields except for SM. Initial SM conditions can be divided into three different types: wet, dry and very dry. Values corresponding to initial SM states have been calculated by combining the WRF soil texture map along with the Soil Moisture Index (SMI). For wet conditions, SMI = 1 has been assigned; for dry conditions, SMI = -0.5; and for very dry conditions, SMI = -1. For a grid point with a given texture class, field capacity, wilting point and SMI are used to obtain initial SM. Two different initial dates have been taken into account to also consider the effect of initializing at different moments in the year: 1990-01-01 00:00:00 UTC and 1990-07-01 00:00:00 UTC. Therefore, 6 experimental runs have been carried out (2 initial dates x 3 initial SM). Additionally, a control run full-driven with ERA-Interim has been conducted from 1982 to 2000 to be used as reference. In this context, the impact of initial conditions on different extreme precipitation indices (R5xDay, SDII and R10mm) and on the Standardized Precipitation Index (SPI) for drought has been addressed.</p><p>Results could help to better understand the relevance of land-atmosphere processes in climate modeling, particularly in assessing WRF sensitivity to variations in SM and its skill to detect drought and precipitation extremes. This information could be notably useful in those applications in which initial conditions are especially relevant, such as the seasonal-to-decadal climate prediction.</p><p>Keywords: soil moisture, initial conditions, precipitation extremes, drought, regional climate, Weather Research and Forecasting model</p><p>ACKNOWLEDGEMENTS: JJRC acknowledges the Spanish Ministry of Science, Innovation and Universities for the predoctoral fellowship (grant code: PRE2018-083921). This research has been carried out in the framework of the projects CGL2017-89836-R, funded by the Spanish Ministry of Economy and Competitiveness with additional FEDER funds, and B-RNM-336-UGR18, funded by FEDER / Junta de Andalucía - Ministry of Economy and Knowledge.</p>

scholarly journals Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

2016 ◽  
Vol 18 (1) ◽  
pp. 5-23 ◽  
Author(s):  
Xuejun Zhang ◽  
Qiuhong Tang ◽  
Xingcai Liu ◽  
Guoyong Leng ◽  
Zhe Li
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Climate Model ◽  
Initial Conditions ◽  
Dry Season ◽  
Drought Monitoring ◽  
Southwestern China ◽  
Forecast Errors ◽  
Wet Season ◽  
Drought Prediction

Abstract In this paper, an experimental soil moisture drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity model (VIC) over southwestern China (SW) is presented. Satellite precipitation data are used to force VIC for a near-real-time estimate of land surface hydrologic conditions. Initialized with satellite-aided monitoring (MONIT), the climate model (CFSv2)-based forecast (MONIT+CFSv2) and ensemble streamflow prediction (ESP)-based forecast (MONIT+ESP) are both performed. One dry season drought and one wet season drought are employed to test the ability of this framework in terms of real-time tracking and predicting the evolution of soil moisture (SM) drought, respectively. The results show that the skillful CFSv2 climate forecasts (CFs) are only found at the first month. The satellite-aided monitoring is able to provide a reasonable estimate of forecast initial conditions (ICs) in real-time mode. In the presented cases, MONIT+CFSv2 forecast exhibits comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1 month. Compared to MONIT+ESP, MONIT+CFSv2 ensembles give more skillful SM drought forecast during the dry season, as indicated by a smaller ensemble range, while the added value of MONIT+CFSv2 is marginal during the wet season. A quantitative attribution analysis of SM forecast uncertainty demonstrates that SM forecast skill is mostly controlled by ICs at the first month and that uncertainties in CFs have the largest contribution to SM forecast errors at longer lead times. This study highlights a value of this framework in generating near-real-time ICs and providing a reliable SM drought prediction with 1 month ahead, which may greatly benefit drought diagnosis, assessment, and early warning.

scholarly journals Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 253
Author(s):  
Luying Ji ◽  
Qixiang Luo ◽  
Yan Ji ◽  
Xiefei Zhi
Keyword(s):  
Northern Hemisphere ◽  
Geopotential Height ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Height Distribution ◽  
Height Field ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Geopotential Height Field

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

scholarly journals Root Response to Soil Water Status via Interaction of Crop Genotype and Environment

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 708
Author(s):  
Phanthasin Khanthavong ◽  
Shin Yabuta ◽  
Hidetoshi Asai ◽  
Md. Amzad Hossain ◽  
Isao Akagi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Distribution ◽  
Water Status ◽  
Soil Layer ◽  
Shoot Biomass ◽  
Soil Conditions ◽  
Crop Adaptation ◽  
Soil Moisture Status ◽  
Root Distributions

Flooding and drought are major causes of reductions in crop productivity. Root distribution indicates crop adaptation to water stress. Therefore, we aimed to identify crop roots response based on root distribution under various soil conditions. The root distribution of four crops—maize, millet, sorghum, and rice—was evaluated under continuous soil waterlogging (CSW), moderate soil moisture (MSM), and gradual soil drying (GSD) conditions. Roots extended largely to the shallow soil layer in CSW and grew longer to the deeper soil layer in GSD in maize and sorghum. GSD tended to promote the root and shoot biomass across soil moisture status regardless of the crop species. The change of specific root density in rice and millet was small compared with maize and sorghum between different soil moisture statuses. Crop response in shoot and root biomass to various soil moisture status was highest in maize and lowest in rice among the tested crops as per the regression coefficient. Thus, we describe different root distributions associated with crop plasticity, which signify root spread changes, depending on soil water conditions in different crop genotypes as well as root distributions that vary depending on crop adaptation from anaerobic to aerobic conditions.

scholarly journals Impacts of soil moisture level and organic matter content on growth of two Juncus species and Poa pratensis grown under acid soil conditions

Weed Research ◽  
2019 ◽  
Vol 59 (6) ◽  
pp. 490-500
Author(s):  
W Kaczmarek‐Derda ◽  
M Helgheim ◽  
J Netland ◽  
H Riley ◽  
K Wærnhus ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Poa Pratensis ◽  
Acid Soil ◽  
Organic Matter Content ◽  
Matter Content ◽  
Moisture Level ◽  
Soil Conditions ◽  
Soil Moisture Level

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

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