Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development

2022 ◽  
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
System Simulation ◽  
Meteorological Forcing ◽  
Simulation Performance ◽  
Spatially Distributed ◽  
Snow Water ◽  
Snow Conditions ◽  
The University ◽  
Spatially Varying

Abstract Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an Observing System Simulation Experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a Western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010-2020. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from -0.036 to -0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain-snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010

2015 ◽  
Vol 28 (20) ◽  
pp. 8037-8051 ◽  
Author(s):  
L. R. Mudryk ◽  
C. Derksen ◽  
P. J. Kushner ◽  
R. Brown
Keyword(s):  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Temporal Consistency ◽  
Meteorological Forcing ◽  
Temporal Correlations ◽  
Snow Water

Abstract Five, daily, gridded, Northern Hemisphere snow water equivalent (SWE) datasets are analyzed over the 1981–2010 period in order to quantify the spatial and temporal consistency of satellite retrievals, land surface assimilation systems, physical snow models, and reanalyses. While the climatologies of total Northern Hemisphere snow water mass (SWM) vary among the datasets by as much as 50%, their interannual variability and daily anomalies are comparable, showing moderate to good temporal correlations (between 0.60 and 0.85) on both interannual and intraseasonal time scales. Wintertime trends of total Northern Hemisphere SWM are consistently negative over the 1981–2010 period among the five datasets but vary in strength by a factor of 2–3. Examining spatial patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to Arctic and alpine regions. Additionally, the datasets derived using relatively recent reanalyses are strongly correlated with one another and show better correlations with the satellite product [the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow)] than do those using older reanalyses. Finally, a comparison of eight reanalysis datasets over the 2001–10 period shows that land surface model differences control the majority of spread in the climatological value of SWM, while meteorological forcing differences control the majority of the spread in temporal correlations of SWM anomalies.

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.

scholarly journals Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

2021 ◽  
Author(s):  
Vincent Vionnet ◽  
Colleen Mortimer ◽  
Mike Brady ◽  
Louise Arnal ◽  
Ross Brown
Keyword(s):  
Newfoundland And Labrador ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Water Security ◽  
Monitoring And Evaluation ◽  
Surface Models ◽  
Snow Water ◽  
The Government ◽  
Different Sources

Abstract. In situ measurements of snow water equivalent (SWE) – the depth of water that would be produced if all the snow melted – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth and derived bulk snow density are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations, and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020 and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro Quebec were included. CanSWE includes over one million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734372 (Vionnet et al., 2021).

Snow as a source of predictability in seasonal forecasts?

2021 ◽  
Author(s):  
Danny Risto ◽  
Bodo Ahrens ◽  
Kristina Fröhlich
Keyword(s):  
Latent Heat ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Reanalysis Data ◽  
Heat Fluxes ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Predictive Skill ◽  
Temperature Forecast ◽  
Snow Water

<p>Besides the ocean, the land surface is a crucial component for predictability at (sub-)seasonal time scales. While the prediction of 2m temperature up to several months is possible for some maritime regions, continental regions lack predictive skill. Improved representation of the land surface in seasonal forecasting systems could help to close this gap. Snow cover fraction and snow water equivalent (SWE) are essential properties of the land surface. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture. First, we analyse the dynamical relationships between snow, 2m temperature and sensible/latent heat fluxes in reanalysis data in the northern hemisphere. Then we investigate whether these relationships are also present in operational seasonal forecast models provided by Copernicus Climate Change Service (C3S). First results show that the quality of the 2m temperature forecast over continental regions drops sharply after the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. Forecasted anomalies in sensible and latent heat fluxes of continental land surfaces show predictive skill during winter and spring only locally in some places, which reduces potential interactions between snow/land surface and the atmosphere in the models. The goal of this ongoing work is to assess the importance of snow initialisation and parameterisation for seasonal forecasting.</p>

scholarly journals Homogenisation of a gridded snow water equivalent climatology for Alpine terrain: methodology and applications

2014 ◽  
Vol 8 (2) ◽  
pp. 471-485 ◽  
Author(s):  
S. Jörg-Hess ◽  
F. Fundel ◽  
T. Jonas ◽  
M. Zappa
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Data Availability ◽  
Model Systems ◽  
Added Value ◽  
Data Sets ◽  
Atmospheric Models ◽  
Catchment Areas ◽  
Snow Water

Abstract. Gridded snow water equivalent (SWE) data sets are valuable for estimating the snow water resources and verify different model systems, e.g. hydrological, land surface or atmospheric models. However, changing data availability represents a considerable challenge when trying to derive consistent time series for SWE products. In an attempt to improve the product consistency, we first evaluated the differences between two climatologies of SWE grids that were calculated on the basis of data from 110 and 203 stations, respectively. The "shorter" climatology (2001–2009) was produced using 203 stations (map203) and the "longer" one (1971–2009) 110 stations (map110). Relative to map203, map110 underestimated SWE, especially at higher elevations and at the end of the winter season. We tested the potential of quantile mapping to compensate for mapping errors in map110 relative to map203. During a 9 yr calibration period from 2001 to 2009, for which both map203 and map110 were available, the method could successfully refine the spatial and temporal SWE representation in map110 by making seasonal, regional and altitude-related distinctions. Expanding the calibration to the full 39 yr showed that the general underestimation of map110 with respect to map203 could be removed for the whole winter. The calibrated SWE maps fitted the reference (map203) well when averaged over regions and time periods, where the mean error is approximately zero. However, deviations between the calibrated maps and map203 were observed at single grid cells and years. When we looked at three different regions in more detail, we found that the calibration had the largest effect in the region with the highest proportion of catchment areas above 2000 m a.s.l. and that the general underestimation of map110 compared to map203 could be removed for the entire snow season. The added value of the calibrated SWE climatology is illustrated with practical examples: the verification of a hydrological model, the estimation of snow resource anomalies and the predictability of runoff through SWE.

Quantifying Snow Mass Mission Concept Trade-Offs Using an Observing System Simulation Experiment

2019 ◽  
Vol 20 (1) ◽  
pp. 155-173 ◽  
Author(s):  
Camille Garnaud ◽  
Stéphane Bélair ◽  
Marco L. Carrera ◽  
Chris Derksen ◽  
Bernard Bilodeau ◽  
...  
Keyword(s):  
Land Surface ◽  
Simulation Experiment ◽  
Snow Water Equivalent ◽  
System Simulation ◽  
Surface Model ◽  
Trade Offs ◽  
Wet Snow ◽  
Observing System Simulation Experiment ◽  
The Impact ◽  
Snow Mass

Abstract Because of its location, Canada is particularly affected by snow processes and their impact on the atmosphere and hydrosphere. Yet, snow mass observations that are ongoing, global, frequent (1–5 days), and at high enough spatial resolution (kilometer scale) for assimilation within operational prediction systems are presently not available. Recently, Environment and Climate Change Canada (ECCC) partnered with the Canadian Space Agency (CSA) to initiate a radar-focused snow mission concept study to define spaceborne technological solutions to this observational gap. In this context, an Observing System Simulation Experiment (OSSE) was performed to determine the impact of sensor configuration, snow water equivalent (SWE) retrieval performance, and snow wet/dry state on snow analyses from the Canadian Land Data Assimilation System (CaLDAS). The synthetic experiment shows that snow analyses are strongly sensitive to revisit frequency since more frequent assimilation leads to a more constrained land surface model. The greatest reduction in spatial (temporal) bias is from a 1-day revisit frequency with a 91% (93%) improvement. Temporal standard deviation of the error (STDE) is mostly reduced by a greater retrieval accuracy with a 65% improvement, while a 1-day revisit reduces the temporal STDE by 66%. The inability to detect SWE under wet snow conditions is particularly impactful during the spring meltdown, with an increase in spatial RMSE of up to 50 mm. Wet snow does not affect the domain-wide annual maximum SWE nor the timing of end-of-season snowmelt timing in this case, indicating that radar measurements, although uncertain during melting events, are very useful in adding skill to snow analyses.

scholarly journals Evaluation of CLASS Snow Simulation over Eastern Canada

2017 ◽  
Vol 18 (5) ◽  
pp. 1205-1225 ◽  
Author(s):  
Diana Verseghy ◽  
Ross Brown ◽  
Libo Wang
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern Canada ◽  
Fractional Snow Cover ◽  
Forest Regions ◽  
Snow Water ◽  
Snow Model

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

Representation of Snow in the Canadian Seasonal to Interannual Prediction System. Part I: Initialization

2016 ◽  
Vol 17 (5) ◽  
pp. 1467-1488 ◽  
Author(s):  
Reinel Sospedra-Alfonso ◽  
Lawrence Mudryk ◽  
William Merryfield ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Initial Conditions ◽  
Reanalysis Data ◽  
Prediction System ◽  
Snow Water ◽  
System Part ◽  
Surface Scheme

Abstract The ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) to provide realistic forecast initial conditions for snow cover is assessed using in situ measurements and gridded snow analyses. Forecast initial conditions for snow in CanCM3 and CanCM4 employed by CanSIPS are determined by the response of the Canadian Land Surface Scheme (CLASS) used in both models to forcing from model atmospheric fields constrained by assimilation of 6-hourly reanalysis data. These snow initial conditions are found to be representative of the daily climatology of snow water equivalent (SWE) as well as interannual variations in maximum SWE and the timing of snow onset and snowmelt observed at eight in situ measurement sites located across Canada. The level of this agreement is similar to that of three independent gridded snow analyses (MERRA, the European Space Agency’s GlobSnow, and an offline forced version of CLASS). Total Northern Hemisphere snow mass generated by the CanSIPS initialization procedure is larger for both models (especially CanCM3) than in MERRA, mostly because of higher SWE in regions of common snow cover. Globally, the interannual variability of initial SWE is found to correlate highly with that of MERRA in locations with appreciable snow. These initial values are compared to SWE in freely running CanCM3 and CanCM4 simulations produced without data assimilation of atmospheric fields. Differences in climatological SWE relative to MERRA are similar in the freely running and assimilating CanCM3 and CanCM4 simulations, suggesting that inherent model biases are a major contributor to biases in CanSIPS snow initial conditions.

scholarly journals Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model

2009 ◽  
Vol 10 (1) ◽  
pp. 130-148 ◽  
Author(s):  
Benjamin F. Zaitchik ◽  
Matthew Rodell
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Radiation Budget ◽  
Surface Model ◽  
Energy Fluxes ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Abstract Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring.

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