1. The 183-WSL Snow Cover Mask The 183-WSL Snowfall Module
5th International Precipitation Working Group Workshop, October, 2010, Hamburg, Germany
Characterization of snow-covered terrains and detection of snowfall by using the 183-WSL retrieval method
Sante Laviola, Stefania D’Aurizio, and Vincenzo Levizzani
CNR, Institute of Atmospheric Sciences and Climate, Bologna - Italy
Abstract
A method for the characterization of snow-covered terrain and the detection of snowfall exploiting the frequencies between 90 and 190 GHz of the Advanced Microwave Soundi Unit-B (AMSU-B) is proposed. The study aims at enhancing the capabilities of the 183-WSL rain retrieval scheme (Laviola and Levizzani, 2008). Previous studies by Cattani et al. (2009) and Laviola and Levizzani (2009a-b) have shown the skills of the 183-WSL algorithm to discern different types of precipitation via the identification of various hydrometeor phases as the method is highly sensitive both to liquid and solid hydrometeors. The current version of the 183-WSL is improved with a snow cover mask and an experimental snowfall processor (183-WSLSF module). This improvement allows first for classifying ice-covered soils prior to the application of the precipitation retrieval modules and secondly for a categorization of convective, stratiform and snowfall precipitation. The improvement offered by these two processors gives now the possibility to apply the 183-WSL algorithm also in the winter season.
183-WSL Retrieval Scheme
The 183-WSL retrieval scheme is based on a series linear equations that identify rainfall rates over various surfaces. The algorithm is schematically described in figure 1. The first step is dedicated to the ingesting and processing the satellite data stream; here all relevant information, namely BTs, surface type (land/sea/mixed), satellite local zenith angles, and topography, are separated from the overall data stream and arranged as input into the 183-WSL processing chain. At this stage the signal from all pixels over land are verified by the snow cover processor. If snwc=1 the pixels are considered snow-covered and then rejected by the computation otherwise they are considered. For pixels evaluated as precipitating Δwin=(T89 – T150)>k, two distinct equations are applied both over land and sea. Other cascade tests are applied to specify if the selected rainy regions can be categorized as stratiform (183-WSLS), convective (183-WSLC) or snowfall precipitation (183-WSLSF). Furthermore, for the discarded pixels (Δwin<k) a detection is carried out: cloud droplets and water vapor absorption (183-WSLW), cloud liquid water in terms of Liquid Water Path (183-LWP) and Low-Level Water Vapor (LLWV) over water surfaces.
Fig.1
The 183-WSL Snow Cover Mask
The new processor of the 183-WSL method for the assessment of snow cover is based on a series of tests. Physically, the approach exploits the strong scattering at 90, 150 and 190 GHz due to the ice crystals over frozen soils. Although the threshold Δwin=(T89 – T150) > 3 K is used in the 183-WSL retrieval scheme to discern rain/no-rain, in presence of snow-covered terrains it assumes similar values giving large overestimations of retrieved rainrates (fig.2). To mitigate this effect the AMSU-B channel at 190 GHz is used. By considering local dry conditions within the atmospheric layers closer to the ice surface, this frequency, usually sounding the water vapor absorption around 2 km height, peaks closer to the surface. Our experiments take us to develop a series of thresholds based on a combination of the above frequencies with the scope to improve snow cover pixel detection and reduce false rain signals into the retrieval method.
The 183-WSL Snowfall Module
The detection of snowfall is based on a range of thresholds where different combinations of the AMSU-B channels are only applied over precipitating regions as detected by the 183-WSL. By considering as limit of snowfall formation the rainy clouds located from a few hundred meters to 5-6 km, we combine channel sensitivities from 90 and 190 GHz to identify snowy areas in the precipitating cores. NEXRAD data have been used to evaluate the sensitivity of the different AMSU-B channels and assess a series of threshold values to differentiate snowflake attenuation from other hydrometeor contributions. In this work two cases of blizzard over the US (Ferraro et al, 2005; Skofronick-Jackson et al., 2003) are used to test the module 183-WSLSF.
Snow cover
processor
Δ190=(T89 – T190)
Δ190_1=(T150 – T190)
Δwin=(T89 – T150)
Δwin=(T89 – T150) > 3 K
Precipitation signal
False alarm
89 GHz
150 GHz
BT< 230 K
N17-Jan at 1858 UTC
N16-Jan at 1515 UTC
Wet snow
Dry snow
Test-1
Test-2
Test-3
Fig.2
Fig.5
Daily Snow Cover Mask
The severe winter conditions on January 2010 over the Baltic Regions offered the possibility to validate the new snow cover mask of the 183-WSL retrieval method. Our daily products are compared with the Multisensor snow/ice cover maps for Northern Hemisphere, considered as “true” data.
AMSU-B channel responses vs NEXARD dBz
The AMSU-B channels respond differently to the different hydrometeor types. Window channels clearly sense the scattering by ice crystals aloft but are also strongly affected by frozen surface emissivity (fig.5-top). Opaque frequencies, on the contrary, show high sensitivity to the solid precipitation for clouds located in the low-and-middle atmosphere (190 and 186 GHz described by black and green dots, respectively, in fig.5-bottom). No perturbation is revealed in the channel at 184 GHz (yellow dots). To roughly isolate snowflake signature a series of approximation have been assumed: 1) snowfall formation is substantially constrained in the first 5-6 km of atmosphere; 2) during both events all precipitation bulk was formed by snowflakes; 3) all snowfall deposits on the ground.
Daily Snow Cover Mask
Daily mask for snow cover on 27 January The algorithm 183-WSL discriminates wet from dry snow, as we can see in fig.4-a (green and dark green, respectively). The classification of status of hydration of snowflakes are mainly based on sensitivity of threshold Δ190.
183-WSL Snow Cover Mask vs IMS
Figure 3 describes the daily percentages of snow pixel detection of the 183-WSL (3-a), IMS (3-b) and the daily discrepancies of two datasets. As we see, the increasing of slope both in 3-a and in 3-b demonstrates that the 183-WSL snow mask is in general extremely sensitive to the accumulation of snow. Nevertheless, due to the complexity of the scene, the algorithm misclassifies some pixels. Discrepancies tend to decrease up to an almost perfect match on 27 January 2010 (see fig. 4).
Fig.3 a b c
Fig.4
183-WSL Snow Cover Mask a
IMS Snow Cover Mask b
wet-snwc
snwsf
conv_rain
strat_rain
clw
dry-snwc
2004 January 25
AMSU-B at 150 and 184 GHz (top-left) on board to N-15 at 13:09 UTC and NEXRAD reflectivity (top-right). Images below describe the 183-WSL products explained in the legend on the side. The 183-WSLSF detects snowfall corresponding to radar reflectivity between dBZ flagging other pixels as convective.
2001 March 5-6
AMSU-B at 150 and 184 GHz (top-left) on board to N-15 at 23:00 UTC and NEXRAD reflectivity (top-right). Images below describe the 183-WSL products. Note that the X-shape are partially retrieved as convective rain and snowfall are confined to the borders.
Conclusions
The first results of the new retrieval scheme of the 183-WSL algorithm are presented. On the basis of a series of new thresholds, the 183-WSL retrieval method discriminates snow-covered pixels from rainy and no-rainy ones. In particular, the extinction of radiation between 90 and 190 GHz is exploited to detect snow-covered terrain while cascade tests allow for classifying retrieved rainfalls as convective, stratiform and snowfall. Snow cover mask validation by considering IMS snow data for Northern Hemisphere over Baltic Regions have revealed high skills to detect snow-covered terrain and identify snow status in terms of wet or dry snow.
The first results of the 183-WSL snowfall detection module are presented. As a testbed two severe blizzards over the Eastern part of United States were selected. Comparisons with radar images have highlighted the skills of the algorithm in delineating snowfall and have encouraged further investigations specifically in the refinement of the technique by using a probabilistic approach as an alternative of the fixed threshold tests. Uncertainties arise when the retrieval is done over frozen soils where snowfall signatures are drastically masked by the scattering from snow-covered terrain.
References
Cattani, E., F. Torricella, S. Laviola, and V. Levizzani: “On the statistical relationship between cloud optical and microphysical characteristics from AVHRR and rainfall intensity derived from a new AMSU rain algorithm”. Nat. Hazards Earth Syst. Sci., 9, , 2009.
Ferraro, R. R., F. Weng, N. C. Grody, L. Zhao, H. Meng, C. Kongoli, P. Pellegrino, S. Qiu, and C. Dean, 2005: NOAA operational hydrological products derived from the Advanced Microwave Sounding Unit. IEEE Trans. Geosci. Remote Sens., 43,
Laviola, S., and V. Levizzani, "Rain retrieval using the 183 GHz absorption lines," IEEE Proc. MicroRad 2008, p. doi: /MICRAD , 2008.
Laviola, S., and V. Levizzani: “Observing precipitation by means of water vapor absorption lines; a new approach to retrieve rain rates from satellite”. Italian J. Rem. Sensing, 41(3), 39-49, 2009a.
Laviola, S, A. Moscatello, M. M. Miglietta, and V. Levizzani: “Satellite and numerical model investigation of heavy rain events over Central Mediterranean”. Submitted to J. Hydrom., 2009b.
Skofronick-Jackson, G. M., J. A. Weinman, and D.-E. Chang, 2003: Spaceborne passive microwave measurement of snowfall over land. IEEE Geosci. Rem. Sens. Symp., 3,
Acknowledgements
These results were partially improved at Swedish Meteorological and Hydrological Institute during a Visiting Scientist Activity in the framework of EUMETSAT’s SAF-Nowcasting/ and Very Short-Range Forecasting .
Support from the from EUMETSAT’s Satellite Application Facility in support to Hydrology and Operational Water Management are gratefully acknowledged.