scholarly journals Validation of the canadian regional climate model (CRCM) snow cover simulations using remote sensing data

2010 ◽  
Author(s):  
K. Chokmani ◽  
M. Wirtensohn ◽  
M. Bernier
Keyword(s):  
Remote Sensing ◽  
Regional Climate Model ◽  
Snow Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Remote Sensing Data ◽  
Canadian Regional Climate Model ◽  
Sensing Data

scholarly journals New lessons on the Sudd hydrology learned from remote sensing and climate modeling

2006 ◽  
Vol 10 (4) ◽  
pp. 507-518 ◽  
Author(s):  
Y. A. Mohamed ◽  
H. H. G. Savenije ◽  
W. G. M. Bastiaanssen ◽  
B. J .J. M. van den Hurk
Keyword(s):  
Remote Sensing ◽  
Regional Climate Model ◽  
Land Surface ◽  
Evaporation Rate ◽  
Climate Model ◽  
Regional Climate ◽  
Remote Sensing Data ◽  
Open Water ◽  
Groundwater Table ◽  
Water Evaporation

Abstract. Despite its local and regional importance, hydro-meteorological data on the Sudd (one of Africa's largest wetlands) is very scanty. This is due to the physical and political situation of this area of Sudan. The areal size of the wetland, the evaporation rate, and the influence on the micro and meso climate are still unresolved questions of the Sudd hydrology. The evaporation flux from the Sudd wetland has been estimated using thermal infrared remote sensing data and a parameterization of the surface energy balance (SEBAL model). It is concluded that the actual spatially averaged evaporation from the Sudd wetland over 3 years of different hydrometeorological characteristics varies between 1460 and 1935 mm/yr. This is substantially less than open water evaporation. The wetland area appears to be 70% larger than previously assumed when the Sudd was considered as an open water body. The temporal analysis of the Sudd evaporation demonstrated that the variation of the atmospheric demand in combination with the inter-annual fluctuation of the groundwater table results into a quasi-constant evaporation rate in the Sudd, while open water evaporation depicts a clear seasonal variability. The groundwater table characterizes a distinct seasonality, confirming that substantial parts of the Sudd are seasonal swamps. The new set of spatially distributed evaporation parameters from remote sensing form an important dataset for calibrating a regional climate model enclosing the Nile Basin. The Regional Atmospheric Climate Model (RACMO) provides an insight not only into the temporal evolution of the hydro-climatological parameters, but also into the land surface climate interactions and embedded feedbacks. The impact of the flooding of the Sudd on the Nile hydroclimatology has been analysed by simulating two land surface scenarios (with and without the Sudd wetland). The paper presents some of the model results addressing the Sudd's influence on rainfall, evaporation and runoff of the river Nile, as well as the influence on the microclimate. The paper presents a case study that confirms the feasibility of using remote sensing data (with good spatial and poor temporal coverage) in conjunction with a regional climate model. The combined model provides good temporal and spatial representation in a region characterized by extremely scarce ground data.

scholarly journals Application of Satellite Data for Evaluating the Cold Climate Performance of the Canadian Regional Climate Model over Québec, Canada

2014 ◽  
Vol 15 (2) ◽  
pp. 614-630 ◽  
Author(s):  
Libo Wang ◽  
Murray MacKay ◽  
Ross Brown ◽  
Paul Bartlett ◽  
Richard Harvey ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Snow Cover ◽  
Satellite Data ◽  
Land Surface ◽  
Cloud Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Cold Climate ◽  
Canadian Regional Climate Model ◽  
Snow Albedo

Abstract This study evaluates key aspects of the snow cover, cloud cover, and radiation budget simulated by the Canadian Regional Climate Model, version 4 (CRCM4), coupled with two versions of the Canadian Land Surface Scheme (CLASS). CRCM4 coupled with CLASS version 2.7 has been used operationally at Ouranos since 2006, while, more recently, CRCM4 has been coupled experimentally with CLASS 3.5, which includes a number of improvements to the representation of snow cover processes. The simulations showed evidence of a systematic cold temperature bias. Evaluation of cloud cover and radiation fluxes with satellite data suggests this bias is related to insufficient cloud radiative forcing from a combination of underestimated cloud cover, excessive cloud albedo, and too low cloud emissivity in the model. This cold bias is reinforced by a positive snow albedo feedback manifest through earlier snow cover onset in the fall and early winter period. Snow albedo was found to be very sensitive to the treatment of albedo refresh but insignificantly influenced by the partitioning of solid precipitation in CLASS. This study demonstrates that atmospheric forcing can exert a significant impact on the simulation of snow cover and surface albedo. The results highlight the need to evaluate parameterizations in land surface models designed for climate models in fully coupled mode.

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

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