Influence of snow and soil moisture initialization on sub-seasonal predictability and forecast skill in boreal spring

2015 ◽  
Vol 47 (1-2) ◽  
pp. 49-65 ◽  
Author(s):  
Jaison Ambadan Thomas ◽  
Aaron A. Berg ◽  
William J. Merryfield
Keyword(s):  
Soil Moisture ◽  
Forecast Skill ◽  
Seasonal Predictability ◽  
Boreal Spring ◽  
Soil Moisture Initialization

scholarly journals The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill

2011 ◽  
Vol 12 (5) ◽  
pp. 805-822 ◽  
Author(s):  
R. D. Koster ◽  
S. P. P. Mahanama ◽  
T. J. Yamada ◽  
Gianpaolo Balsamo ◽  
A. A. Berg ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Rain Gauge ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Boreal Summer ◽  
Second Phase ◽  
Global Land ◽  
Soil Moisture Initialization ◽  
Coupling Experiment

Abstract The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.

scholarly journals Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

2014 ◽  
Vol 15 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Randal D. Koster ◽  
Gregory K. Walker ◽  
Sarith P. P. Mahanama ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Forecast Skill ◽  
Surface Model ◽  
Streamflow Forecast ◽  
Continental Scale ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow ◽  
Soil Moisture Initialization

Abstract Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relative to precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface–modeling system as a tool for addressing the science of hydrological prediction.

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.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

Performance analysis of regional landslide early warning based on soil moisture simulations

2020 ◽  
Author(s):  
Adrian Wicki ◽  
Manfred Stähli
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Performance Analysis ◽  
Early Warning ◽  
Soil Profile ◽  
Forecast Skill ◽  
Soil Moisture Dynamics ◽  
Landslide Early Warning ◽  
Moisture Dynamics

<p>In mountainous regions, rainfall-triggered landslides pose a serious risk to people and infrastructure, particularly due to the short time interval between activation and failure and their widespread occurrence. Landslide early warning systems (LEWS) have demonstrated to be a valuable tool to inform decision makers about the imminent landslide danger and to move people or goods at risk to safety. While most operational LEWS are based on empirically derived rainfall exceedance thresholds, recent studies have demonstrated an improvement of the forecast quality after the inclusion of in-situ soil moisture measurements.</p><p>The use of in-situ soil moisture sensors bears specific limitations, such as the sensitivity to local conditions, the disturbance of the soil profile during installation, and potential data quality issues and inhomogeneity of long-term measurements. Further, the installation and operation of monitoring networks is laborious and costly. In this respect, making use of modelled soil moisture could efficiently increase information density, and it would further allow to forecast soil moisture dynamics. On the other hand, numerical simulations are restricted by assumptions and simplifications related to the soil hydraulic properties and the water transfer in the soil profile. Ultimately, the question arises how reliable and representative landslide early warnings based on soil moisture simulations are compared to warnings based on measurements.</p><p>To answer this, we applied a state-of-the-art one-dimensional heat and mass transfer model (CoupModel, Jansson 2012) to generate time series of soil water content at 35 sites in Switzerland. The same sites and time period (2008-2018) were used in a previous study to compare the temporal variability of in-situ measured soil moisture to the regional landslide activity (currently under review in <em>Landslides</em>). The same statistical framework for soil moisture dynamics analysis, landslide probability modelling and landslide early warning performance analysis was applied to the modelled and the measured soil moisture time series. This allowed to directly compare the forecast skill of modelling-based with measurements-based landslide early warning.</p><p>In this contribution, we will highlight three steps of model applications: First, a straight-forward simulation to all 35 sites without site-specific calibration and using reference soil layering only, to assess the forecast skill as if no prior measurements were available. Second, a model simulation after calibration at each site using the existing soil moisture time series and information on the soil texture to assess the benefit of a thorough calibration process on the forecast skill. Finally, an application of the model to additional sites in Switzerland where no soil moisture measurements are available to assess the effect of increasing the soil moisture information density on the overall forecast skill.</p>

scholarly journals A Note on Soil Moisture Memory and Interactions with Surface Climate for Different Vegetation Types in the La Plata Basin

2015 ◽  
Vol 16 (2) ◽  
pp. 716-729 ◽  
Author(s):  
Anna A. Sörensson ◽  
Ernesto Hugo Berbery
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Statistical Significance ◽  
Surface Fluxes ◽  
Maximum Temperature ◽  
Seasonal Forecasts ◽  
La Plata ◽  
La Plata Basin ◽  
Surface Climate ◽  
Soil Moisture Initialization

Abstract This work examines the evolution of soil moisture initialization biases and their effects on seasonal forecasts depending on the season and vegetation type for a regional model over the La Plata basin in South America. WRF–Noah simulations covering multiple cases during a 2-yr period are designed to emphasize the conceptual nature of the simulations at the expense of the statistical significance of the results. Analysis of the surface climate shows that the seasonal predictive skill is higher when the model is initialized during the wet season and the initial soil moisture differences are small. Large soil moisture biases introduce large surface temperature biases, particularly for savanna, grassland, and cropland vegetation covers at any time of the year, thus introducing uncertainty in the surface climate. Regions with evergreen broadleaf forest have roots that extend to the deep layer whose moisture content affects the surface temperature through changes in the partitioning of the surface fluxes. The uncertainties of monthly maximum temperature can reach several degrees Celsius during the dry season in cases when 1) the soil is much wetter in the reanalysis than in the WRF–Noah equilibrium soil moisture and 2) the memory of the initial value is long because of scarce rainfall and low temperatures. This study suggests that responses of the atmosphere to soil moisture initialization depend on how the initial wet and dry conditions are defined, stressing the need to take into account the characteristics of a particular region and season when defining soil moisture initialization experiments.

scholarly journals Evaluation of the subseasonal forecast skill of surface soil moisture in the S2S database

2019 ◽  
Vol 12 (6) ◽  
pp. 467-474 ◽  
Author(s):  
Hanchen ZHU ◽  
Haishan CHEN ◽  
Yang ZHOU ◽  
Xuan DONG
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Forecast Skill ◽  
Surface Soil Moisture

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.

Sign in / Sign up

Export Citation Format

Share Document