Identifying Snowfall Elevation Patterns by Assimilating Satellite- Based Snow Depth Retrievals

Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation. Here, we propose a novel approach to characterize orographic snowfall over mountain regions. We use a particle batch smoother to leverage satellite information from Sentinel-1 derived snow depth retrievals and to correct various gridded precipitation products. This novel approach is tested using a simple snow model for an alpine basin located in Trentino Alto Adige, Italy. We quantify the precipitation biases across the basin and found that the assimilation method (i) corrects for snowfall biases and uncertainties, (ii) leads to cumulative snowfall elevation patterns that are consistent across precipitation products, and (iii) results in overall improved basin-wide snow variables (snow depth and snow cover area) and basin streamflow estimates.

The analysis of the snowfall elevation patterns' spatial characteristics indicates that the proposed assimilation scheme results in more accurate spatial patterns in the snowfall distribution across the entire basin. The derived snowfall orographic patterns contribute to a comprehensive improvement of mountain hydrologic variables such as snow depth, snow cover area, and streamflow. The most significant enhancements in streamflow are observed during the spring and summer months when peak flow observations align more accurately with the posterior cases than the prior ones. These results primarily stem from the fact that the assimilation of Sentinel-1 assigns less snowfall to the lower-elevation regions of the basin, while higher rates are assigned to the higher elevation. As summer approaches, water is released more slowly from the higher elevation via snow-melt than in the prior case, which aligns better with observations. The assimilation of Sentinel-1 effectively downscales coarser-resolution precipitation products. While the prior snowfall cumulative elevation pattern has a small gradient across elevation bands, these patterns are consistent across elevations and precipitation products after the assimilation of snow depth retrievals. In conclusion, this study provides a framework for correcting snowfall orographic patterns across other seasonally-snow dominated mountain areas of the world, especially where in-situ data are scarce. The full paper can be found by clicking on the Figure above.
Reference


Girotto, Manuela, Giuseppe Formetta, Shima Azimi, Claire Bachand, Marianne Cowherd, Gabrielle De Lannoy, Hans Lievens, et al. 2023. “Identifying Snowfall Elevation Patterns by Assimilating Satellite-Based Snow Depth Retrievals.” The Science of the Total Environment, September, 167312. https://doi.org/10.1016/j.scitotenv.2023.167312.

A method for determining optimal observations for prediction

This is the seminar given in Trento on May 30th by Henk Dijkstra (GS). Henk is mainly an oceanographer but the methods he illustrates, especially the Bayesian tools he develops towards the end of his presentation can be useful also in hydrological cases, so I am very happy to host his talk here.

The discussion that followed is here:

The slides of the talk are here. And here is the paper by Kramer et al. (JPO 2012), Measuring the Impact of Observations on the Predictability of the Kuroshio Extension in a Shallow-Water Model.

Using geostatistics to integrate satellite information and modelling on soil moisture

This paper has a long history and explore the idea that geostatistics can be used to integrate satellite information when this is missing. At the same time the whole information is used for assimilated for better driving the Community Land Model. Thank you to Han Xujun for pursuing the publication, when I abandoned any hope, notwithstanding that the paper is a good one.

The paper is entitled: Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization, and my co-authors are (in order):
Han Xujun, Xin Li, Rui Jin, and Stefano Endrizzi.  The paper has been accepted by PLOSONE, and you can find the pre-print  here.

Other papers by Xujun are available from his Research Gate Profile.

A little of further bibliography:

Han, X. J., et al. (2014). "Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations." Water Resources Research 50(7): 6081-6105.

Han, X. J., et al. (2013). "Joint Assimilation of Surface Temperature and L-Band Microwave Brightness Temperature in Land Data Assimilation." Vadose Zone Journal 12(3).

Han, X., et al. (2012). "Spatial horizontal correlation characteristics in the land data assimilation of soil moisture." Hydrology and Earth System Sciences 16(5): 1349-1363.