Ali Nadir Arslan

Observing seasonal snow changes in the boreal forest area using active and passive microwave measurements

We present initial results from an experimental campaign aiming to acquire a comprehensive, full-snow season dataset of simultaneous backscatter and brightness temperature measurements of snow covered ground. The campaign is a part of Phase A activities in support of the proposed CoReH2O mission, aiming both to contribute to investigations on interpreting snow properties from active microwave observations, and to explore the possibilities for synergistic use of active measurements with existing passive microwave instruments. The campaign period covers the winter season of 2009-2010. Microwave observations are complemented by detailed in situ data of snow cover properties.

New Snow Water Equivalent Processing System With Improved Resolution Over Europe and its Applications in Hydrology

The presence and amount of snow, given in terms of snow water equivalent (SWE), is an essential physical characteristic influencing climate and hydrological processes. For the recent decades, remote sensing has proven to be a valuable tool for deriving regional and global scale information on SWE. However, determining SWE reliably from remote sensing data for many local-scale applications remains a challenge. Microwave radiometers are currently the best option to determine SWE since they respond to snow depth and density. Further, weather phenomena and solar illumination are not of concern. However, for some purposes the typical spatial resolution of space-borne radiometers (in the order of tens of kilometers) is not sufficient. In this study, the spatial resolution of existing operational SWE products (GlobSnow and H-SAF product portfolios) is improved by performing assimilation of ground truth observations of snow depth and space borne derived SWE estimates in a resolution grid of 0.05° × 0.05° (approximately 5 km × 5 km). Some modifications to the SWE algorithm and the applied auxiliary data (such as an improved forest stem volume map) are introduced. We will present how the improved resolution enhances spatial details in the retrieved SWE, while the validation results show that in terms of accuracy, the new product is on similar level than the existing operational products. Finally, the gained new SWE estimates are ingested into the HOPS hydrological model in the Ounasjoki river basin. The results indicate that simulation of snow melt driven river discharge can be improved by ingesting the retrieved SWE data into a hydrological model.

Analysis of active and passive microwave observations from the NoSREx campaign

The acquisition of Snow Water Equivalent (SWE) at spatial resolutions higher than those of the present methods relying on inversion of coarse-scale passive microwave observations is a possible application for space-borne SAR imagery. The presented experimental campaign NoSREx (Nordic Snow Radar Experiment) was initiated to contribute to the knowledge of snowpack backscattering and emission properties, in particular, to help develop methods to retrieve SWE from high-resolution two-frequency SAR observations (at X and Ku band). Another objective was to provide data for studies exploring the synergistic use of active and passive microwave observations for monitoring of snow properties. The NoSREx campaign began in November 2009, and has recently concluded a second winter period of observations.

Hydrological applications of super resolution SWE processing system over Europe

Reliable global and regional scale SWE maps can be calculated by the assimilation of space borne derived SWE estimates and ground based SD observations. The spatial resolution of these products is ~25 km per pixel which is good enough for climate research but for hydrology a higher resolution is often optimal. A regional SWE processing system with nominal resolution of ~ 5 km per pixel over Europe is described in this paper. In addition the validation results show that the sensitivity to SWE is on the same level as with the lower resolution products. SWE data are also assimilated with HOPS hydrological model and the results show an improvement in river discharge estimates.

SNOWCARBO: Monitoring and assessment of carbon balance related phenomena in Finland and northern Eurasia

SnowCarbo project is funded by the European commission (EC) Life+ program. The project was started at the beginning of January 2009 and it will end in December 2012. Coordinating Beneficiary of SnowCarbo project is Finnish Meteorological Institute (FMI) and Associated Beneficiary(ies) are Finnish Environment Institute (SYKE), and Commissariat a lénergie atomique - Laboratoire des Sciences du Climat et de lEnvironnement (CEA-LSCE). The main objective of the Snowcarbo project is to implement and demonstrate a new innovative approach for the net CO2 balance mapping in northern Finland and northern Eurasian region. The approach employs connected REMO regional climate and JSBACH ecosystem models [1,2,3] and is based on a combination of different information sources describing snow evolution, phenology, land cover, CO2 fluxes and concentrations. The implemented method combines local in-situ observations and global Earth Observation satellite data together with land cover class information in a new way. Snowcarbo aims to produce carbon dioxide balance maps over northern Finland and northern Eurasia by combining different earth observation data sources and modeling of CO2 balance.

Effects of snowpack parameters and layering processes at X- and Ku-band backscatter

In this paper, how typical snowpack parameters with layering processes affect to the sensitivity of X- and Ku-band backscatter to the increase of SWE (Snow Water Equivalent) was analyzed. A particular motivation of this work was to contribute to the development of the geophysical algorithm of CoReH20, a proposed ESA SAR mission currently in Phase A [1][2]. DSLDMRT forward backscatter model for microwave backscatter from snow covered terrain was used in analysis. The software is based on a second order radiative transfer model using the dense medium approach [3]. The analyses showed that the layering of snowpack changes the sensitivity of backscatter to SWE. A layer of refrozen at the bottom of snow pack (resulting from thaw-refreeze cycles at early winter) can cause a negative correlation of backscatter with the increase SWE for the beginning of the dry snow accumulation period. The positive correlation between snow grain size and SWE, typical for the temporal metamorphosis, increases the correlation between SWE and backscattering coefficient.

Revising the land cover and use classification of northern areas for climate modeling

Today, different carbon sources are producing more carbon dioxide than is being absorbed by carbon sinks, contributing towards the instability in the natural balance of carbon dioxide. The goal of the SnowCarbo-project is to improve the model predictions of carbon dioxide by using a variety of Earth Observation, GIS and in situ data in constraining and calibrating the models. The aim of this article is to present different alternatives for land cover data needed in climate and carbon balance modeling, and some preliminary evaluation in the context of climate modeling. The regional climate model REMO developed at Max Planck Institute has been used to simulate the past, present and future climates over wide range of spatial resolutions. These models use Olson ecosystem classification as land cover data, which represents Finnish environment quite badly. Therefore, new versions of land cover data have been constructed based on higher resolution GlobCover and Corine Land Cover classifications as well as classifying different MODIS-products. The results are preliminary, but new versions seem to work better.