Summer Rainfall Forecast Spread in an Ensemble Initialized with Different Soil Moisture Analyses

2007 ◽  
Vol 22 (2) ◽  
pp. 299-314 ◽  
Author(s):  
Eric A. Aligo ◽  
William A. Gallus ◽  
Moti Segal
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Warm Season ◽  
Summer Rainfall ◽  
Rainfall Threshold ◽  
Rainfall Thresholds ◽  
Relative Operating Characteristic ◽  
The North ◽  
Forecasting System

Abstract The performance of an ensemble forecasting system initialized using varied soil moisture alone has been evaluated for rainfall forecasts of six warm season convective cases. Ten different soil moisture analyses were used as initial conditions in the ensemble, which used the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model at 4-km horizontal grid spacing with explicit rainfall. Soil moisture analyses from the suite of National Weather Service operational models—the Rapid Update Cycle, the North American Model (formerly known as the Eta Model), and the Global Forecasting System—were used to design the 10-member ensemble. For added insight, two other runs with extremely low and high soil moistures were included in this study. Although the sensitivity of simulated 24-h rainfall to soil moisture was occasionally substantial in both weakly forced and strongly forced cases, a U-shaped rank histogram indicated insufficient spread in the 10-member ensemble. This result suggests that ensemble forecast systems using soil moisture perturbations alone might not add enough variability to rainfall forecasts. Perturbations to both atmospheric initial conditions and land surface initial conditions as well as perturbations to other aspects of model physics may increase forecast spread. Correspondence ratio values for the 0.01- and 0.5-in. rainfall thresholds imply some spread in the soil moisture ensemble, but mainly in the weakly forced cases. Relative operating characteristic curves for the 10-member ensemble and for various rainfall thresholds indicate modest skill for all thresholds with the most skill associated with the lowest rainfall threshold, a result typical of warm season events.

scholarly journals Drought Relief and Reversal over North America from 1500 to 2016

2021 ◽  
Vol 25 (1) ◽  
pp. 94-107
Author(s):  
M. C. A. Torbenson ◽  
D. W. Stahle ◽  
I. M. Howard ◽  
D. J. Burnette ◽  
D. Griffin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Land Surface ◽  
Large Scale ◽  
Temporal Stability ◽  
Warm Season ◽  
Cool Season ◽  
Continental Scale ◽  
The North ◽  
Drought Relief

Abstract Season-to-season persistence of soil moisture drought varies across North America. Such interseasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool-season (December–April) and warm-season (May–July) precipitation series and offers new insights on the intra-annual changes in drought for up to 2000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and 3-month-long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool-season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the twentieth century and are used to estimate relief and reversals for the most skillfully reconstructed past 500 years. Subcontinental-to-continental-scale reversals of cool-season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the mid-Atlantic region and the U.S. Southwest. Drought relief at the continental scale may arise in part from macroscale ocean–atmosphere processes, whereas the smaller-scale regional reversals may reflect land surface feedbacks and stochastic variability.

scholarly journals Early warm-season mesoscale convective systems dominate soil moisture–precipitation feedback for summer rainfall in central United States

2021 ◽  
Vol 118 (43) ◽  
pp. e2105260118
Author(s):  
Huancui Hu ◽  
L. Ruby Leung ◽  
Zhe Feng
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Warm Season ◽  
Summer Rainfall ◽  
Mesoscale Convective Systems ◽  
Rainfall Events ◽  
Convective Systems ◽  
Central United States ◽  
Mesoscale Convective

Land–atmosphere interactions play an important role in summer rainfall in the central United States, where mesoscale convective systems (MCSs) contribute to 30 to 70% of warm-season precipitation. Previous studies of soil moisture–precipitation feedbacks focused on the total precipitation, confounding the distinct roles of rainfall from different convective storm types. Here, we investigate the soil moisture–precipitation feedbacks associated with MCS and non-MCS rainfall and their surface hydrological footprints using a unique combination of these rainfall events in observations and land surface simulations with numerical tracers to quantify soil moisture sourced from MCS and non-MCS rainfall. We find that early warm-season (April to June) MCS rainfall, which is characterized by higher intensity and larger area per storm, produces coherent mesoscale spatial heterogeneity in soil moisture that is important for initiating summer (July) afternoon rainfall dominated by non-MCS events. On the other hand, soil moisture sourced from both early warm-season MCS and non-MCS rainfall contributes to lower-level atmospheric moistening favorable for upscale growth of MCSs at night. However, soil moisture sourced from MCS rainfall contributes to July MCS rainfall with a longer lead time because with higher intensity, MCS rainfall percolates into deeper soil that has a longer memory. Therefore, early warm-season MCS rainfall dominates soil moisture–precipitation feedback. This motivates future studies to examine the contribution of early warm-season MCS rainfall and associated soil moisture anomalies to predictability of summer rainfall in the major agricultural region of the central United States and other continental regions frequented by MCSs.

scholarly journals Spatial variability and its scale dependency of observed and modeled soil moisture over different climate regions

2013 ◽  
Vol 17 (3) ◽  
pp. 1177-1188 ◽  
Author(s):  
B. Li ◽  
M. Rodell
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Land Surface ◽  
Large Scale ◽  
Warm Season ◽  
Scale Dependency ◽  
Convex Shape ◽  
Spatial Moments ◽  
Environmental Controls

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

scholarly journals Assessment of Rainfall Thresholds for Rain-Induced Landslide Activity in North Sikkim Road Corridor in Sikkim Himalaya, India

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Bappaditya Koley ◽  
Anindita Nath ◽  
Subhajit Saraswati ◽  
Kaushik Bandyopadhyay ◽  
Bidhan Chandra Ray
Keyword(s):  
Rainfall Intensity ◽  
Lower Boundary ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Rainfall Data ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Sikkim Himalaya ◽  
North East ◽  
The North

Land sliding is a perennial problem in the Eastern Himalayas. Out of 0.42 million km2 of Indian landmass prone to landslide, 42% fall in the North East Himalaya, specially Darjeeling and Sikkim Himalaya. Most of these landslides are triggered by excessive monsoon rainfall between June and October in almost every year. Various attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity – duration of antecedent rainfall models on global, regional and local scale for triggering of the landslide. This paper describes local aspect of rainfall threshold for landslides based on daily rainfall data in and around north Sikkim road corridor region. Among 210 Landslides occurring from 2010 to 2016 were studied to analyze rainfall thresholds. Out of the 210 landslides, however, only 155 Landslides associated with rainfall data which were analyzed to yield a threshold relationship between rainfall intensity-duration and landslide initiation. The threshold relationship determined fits to lower boundary of the Landslide triggering rainfall events is I = 4.045 D - 0.25 (I=rainfall intensity (mm/h) and D=duration in (h)), revealed that for rainfall event of short time (24 h) duration with a rainfall intensity of 1.82 mm/h, the risk of landslides on this road corridor of the terrain is expected to be high. It is also observed that an intensity of 58 mm and 139 mm for 10-day and 20-day antecedent rainfall are required for the initiation of landslides in the study area. This threshold would help in improvement on traffic guidance and provide safety to the travelling tourists in this road corridor during the monsoon.

Sensitivity of the PBL and Precipitation in 12-Day Simulations of Warm-Season Convection Using Different Land Surface Models and Soil Wetness Conditions

2008 ◽  
Vol 136 (7) ◽  
pp. 2321-2343 ◽  
Author(s):  
S. B. Trier ◽  
F. Chen ◽  
K. W. Manning ◽  
M. A. LeMone ◽  
C. A. Davis
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Warm Season ◽  
Land Surface Models ◽  
Surface Conditions ◽  
Late Afternoon ◽  
Surface Models ◽  
Initial Soil ◽  
Soil Wetness

Abstract A coupled land surface–atmospheric model that permits grid-resolved deep convection is used to examine linkages between land surface conditions, the planetary boundary layer (PBL), and precipitation during a 12-day warm-season period over the central United States. The period of study (9–21 June 2002) coincided with an extensive dry soil moisture anomaly over the western United States and adjacent high plains and wetter-than-normal soil conditions over parts of the Midwest. A range of possible atmospheric responses to soil wetness is diagnosed from a set of simulations that use land surface models (LSMs) of varying sophistication and initial land surface conditions of varying resolution and specificity to the period of study. Results suggest that the choice of LSM [Noah or the less sophisticated simple slab soil model (SLAB)] significantly influences the diurnal cycle of near-surface potential temperature and water vapor mixing ratio. The initial soil wetness also has a major impact on these thermodynamic variables, particularly during and immediately following the most intense phase of daytime surface heating. The soil wetness influences the daytime PBL evolution through both local and upstream surface evaporation and sensible heat fluxes, and through differences in the mesoscale vertical circulation that develops in response to horizontal gradients of the latter. Resulting differences in late afternoon PBL moist static energy and stability near the PBL top are associated with differences in subsequent late afternoon and evening precipitation in locations where the initial soil wetness differs among simulations. In contrast to the initial soil wetness, soil moisture evolution has negligible effects on the mean regional-scale thermodynamic conditions and precipitation during the 12-day period.

Pentad Evolution of the 1988 Drought and 1993 Flood over the Great Plains: An NARR Perspective on the Atmospheric and Terrestrial Water Balance

2009 ◽  
Vol 22 (20) ◽  
pp. 5366-5384 ◽  
Author(s):  
Scott J. Weaver ◽  
Alfredo Ruiz-Barradas ◽  
Sumant Nigam
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Great Plains ◽  
Land Surface ◽  
Surface States ◽  
Moisture Flux ◽  
Summer Drought ◽  
Southern Plains ◽  
The North ◽  
Terrestrial Water

Abstract The evolution of the atmospheric and land surface states during extreme hydroclimate episodes over North America is investigated using the North American Regional Reanalysis (NARR), which additionally, and successfully, assimilates precipitation. The pentad-resolution portrayals of the atmospheric and terrestrial water balance over the U.S. Great Plains during the 1988 summer drought and the July 1993 floods are analyzed to provide insight into the operative mechanisms including regional circulation (e.g., the Great Plains low-level jet, or GPLLJ) and hydroclimate (e.g., precipitation, evaporation, soil moisture recharge, runoff). The submonthly (but supersynoptic time scale) fluctuations of the GPLLJ are found to be very influential, through related moisture transport and kinematic convergence (e.g., ∂υ/∂y), with the jet anomalies in the southern plains leading the northern precipitation and related moisture flux convergence, accounting for two-thirds of the dry and wet episode precipitation amplitude. The soil moisture influence on hydroclimate evolution is assessed to be marginal as evaporation anomalies are found to lag precipitation ones, a lead–lag not discernible at monthly resolution. The pentad analysis thus corroborates the authors’ earlier findings on the importance of transported moisture over local evaporation in Great Plains’ summer hydroclimate variability. The regional water budgets—atmospheric and terrestrial—are found to be substantially unbalanced, with the terrestrial imbalance being unacceptably large. Pentad analysis shows the atmospheric imbalance to arise from the sluggishness of the NARR evaporation, including its overestimation in wet periods. The larger terrestrial imbalance, on the other hand, has its origins in the striking unresponsiveness of the NARR’s runoff, which is underestimated in wet episodes. Finally, the influence of ENSO and North Atlantic Oscillation (NAO) variability on the GPLLJ is quantified during the wet episode, in view of the importance of moisture transports. It is shown that a significant portion (∼25%) of the GPLLJ anomaly (and downstream precipitation) is attributable to NAO and ENSO’s influence, and that this combined influence prolongs the wet episode beyond the period of the instigating GPLLJ.

scholarly journals Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins

2020 ◽  
Author(s):  
Yifan Zhou ◽  
Benjamin F. Zaitchik ◽  
Sujay V. Kumar ◽  
Kristi R. Arsenault ◽  
Mir A. Matin ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Land Surface ◽  
Root Zone ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Earth Observing System ◽  
South And Southeast Asia ◽  
Forecasting System ◽  
Land Information System

Abstract. South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth Observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end-users across the region. This system applies the Noah-MultiParameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model - sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5-km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made one to two months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

scholarly journals LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the CONtiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

2018 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Aymeric Bocher ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
The United States ◽  
Agricultural Drought ◽  
Reference Network ◽  
Surface Model ◽  
Area Index ◽  
Medium Range Weather Forecast ◽  
The Impact

LDAS-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) estimates to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) Land Surface Model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center For Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a 7-yr, quarter degree spatial resolution offline reanalysis of Land Surface Variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Also, taking advantage of the relatively dense data networks over CONUS, we also evaluate the impact of the assimilation against in-situ measurements of soil moisture from the USCRN network (US Climate Reference Network) are used in the evaluation, together with river discharges from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared to an open-loop simulation (i.e. no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions which impacts persist through time. LDAS-Monde reanalysis has a potential to be used to monitor extreme events like agricultural drought, also. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

scholarly journals Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

2019 ◽  
Author(s):  
Clément Albergel ◽  
Emanuel Dutra ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Simon Munier ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Western Europe ◽  
Initial Conditions ◽  
Added Value ◽  
Area Index ◽  
Long Time ◽  
Land Data Assimilation System ◽  
The Impact

This study aims to assess the potential of the LDAS-Monde a land data assimilation system developed by Météo-France to monitor the impact of the 2018 summer heatwave over western Europe vegetation state. The LDAS-Monde is forced by the ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). Analysis of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of North Western Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the considered domain have never been observed in the LAI product over this 18-yr period. The LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25°x0.25° (January 2008 to October 2018) and 0.10°x0.10° (April 2016 to December 2018). Both configuration of the LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhance the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedbacks in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model only initial conditions.

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.

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