scholarly journals The impact of the melting layer on the passive microwave cloud scattering signal observed from satellites: A study using TRMM microwave passive and active measurements

2013 ◽  
Vol 118 (11) ◽  
pp. 5667-5678 ◽  
Author(s):  
V. S. Galligani ◽  
C. Prigent ◽  
E. Defer ◽  
C. Jimenez ◽  
P. Eriksson
Keyword(s):  
Passive Microwave ◽  
Melting Layer ◽  
Active Measurements ◽  
The Impact ◽  
Scattering Signal

scholarly journals USING SMOS PASSIVE MICROWAVE DATA TO DEVELOP SMAP FREEZE/THAW ALGORITHMS ADAPTED FOR THE CANADIAN SUBARCTIC

2015 ◽  
Vol XL-1-W5 ◽  
pp. 365-368 ◽  
Author(s):  
P. Kalantari ◽  
M. Bernier ◽  
K. C. McDonal ◽  
J. Poulin
Keyword(s):  
Land Cover ◽  
Time Series Data ◽  
Passive Microwave ◽  
Soil Freezing ◽  
Series Data ◽  
Freezing And Thawing ◽  
Freeze Thaw ◽  
Thaw Cycle ◽  
Freeze Thaw Cycle ◽  
The Impact

Seasonal terrestrial Freeze/Thaw cycle in Northern Quebec Tundra (Nunavik) was determined and evaluated with passive microwave observations. SMOS time series data were analyzed to examine seasonal variations of soil freezing, and to assess the impact of land cover on the Freeze/Thaw cycle. Furthermore, the soil freezing maps derived from SMOS observations were compared to field survey data in the region near Umiujaq. The objective is to develop algorithms to follow the seasonal cycle of freezing and thawing of the soil adapted to Canadian subarctic, a territory with a high complexity of land cover (vegetation, soil, and water bodies). Field data shows that soil freezing and thawing dates vary much spatially at the local scale in the Boreal Forest and the Tundra. The results showed a satisfactory pixel by pixel mapping for the daily soil state monitoring with a > 80% success rate with in situ data for the HH and VV polarizations, and for different land cover. The average accuracies are 80% and 84% for the soil freeze period, and soil thaw period respectively. The comparison is limited because of the small number of validation pixels.

scholarly journals Variations in snowpack melt on the Greenland ice sheet based on passive-microwave measurements

1995 ◽  
Vol 41 (137) ◽  
pp. 51-60 ◽  
Author(s):  
Thomas L. Mote ◽  
Mark R. Anderson
Keyword(s):  
Climate Change ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ablation Rate ◽  
Passive Microwave ◽  
Microwave Emission ◽  
The Arctic ◽  
Emission Model ◽  
The Impact ◽  
Microwave Data

AbstractA simple microwave-emission model is used to simulate 37 GHz brightness temperatures associated with snowpack-melt conditions for locations across the Greenland ice sheet. The simulated values are utilized as threshold values and compared to daily, gridded SMMR and SSM/I passive-microwave data, in order to reveal regions experiencing melt. The spatial extent of the area classified as melting is examined on a daily, monthly and seasonal (May-August) basis for 1979–91. The typical seasonal cycle of melt coverage shows melt beginning in late April, a rapid increase in the melting area from mid-May to mid-July, a rapid decrease in melt extent from late July through mid-August, and cessation of melt in late September. Seasonal averages of the daily melt extents demonstrate an apparent increase in melt coverage over the 13 year period of approximately 3.8% annually (significant at the 95% confidence interval). This increase is dominated by statistically significant positive trends in melt coverage during July and August in the west and southwest of the ice sheet. We find that a linear correlation between microwave-derived melt extent and a surface measure of ablation rate is significant in June and July but not August, so caution must be exercised in using the microwave-derived melt extents in August. Nevertheless, knowledge of the variability of snowpack melt on the Greenland ice sheet as derived from microwave data should prove useful in detecting climate change in the Arctic and examining the impact of climate change on the ice sheet.

LOW-ENERGY 4He+ SCATTERING FROM DEUTERIUM ADSORBED ON STEPPED Pd(331)

1997 ◽  
Vol 04 (06) ◽  
pp. 1305-1308 ◽  
Author(s):  
W. P. ELLIS ◽  
R. BASTASZ
Keyword(s):  
Incidence Angle ◽  
Forward Scattering ◽  
Low Energy ◽  
Quasielastic Scattering ◽  
Scattering Angle ◽  
Octahedral Void ◽  
Scattering Study ◽  
Adsorption Geometry ◽  
The Impact ◽  
Scattering Signal

We have taken angle-resolved data for the scattering of low-energy (<1 keV) 4 He + from deuterium adsorbed on a stepped Pd (331) surface. The impact geometry was "up the staircase," i.e. the 4 He + beam was perpendicular to and directly incident onto the unshadowed <011> Pd ledge atoms. A strong quasielastic scattering signal of 4 He + from D (4 He +/D) was observed at a forward-scattering angle of θ= 25° and an incidence angle of α= 76° from the (331) normal. The results agree with shadow-cone calculations of scattering first from Pd ledge atoms followed by a second event, 4 He +/D. The resultant adsorption geometry shows D to reside in the quasi-threefold ledge site on the surface directly above the bulk fcc octahedral void. These results are consistent with the previous 4 He + scattering study of the geometrically related Pd(110)-D(ads) system.

scholarly journals Analysis of Z–R Relations Based on LDR Signatures within the Melting Layer

2010 ◽  
Vol 27 (9) ◽  
pp. 1555-1561 ◽  
Author(s):  
Stefan Kowalewski ◽  
Gerhard Peters
Keyword(s):  
Empirical Relation ◽  
Future Research ◽  
Melting Layer ◽  
Weather Radars ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Linear Depolarization Ratio ◽  
Layer Region ◽  
The Impact ◽  
R Relation

Abstract The inclusion of polarimetric measurements for the quantitative precipitation estimation (QPE) by weather radars as well as space- and airborne radars is considered most promising now-a-days. Because the melting layer region is usually marked by a distinct peak of the linear depolarization ratio (LDR), a possible correlation between LDR peak values and underlying drop sizes in terms of the Z–R relation is investigated, that is, the empirical relation between radar reflectivity factor Z and rain rate R. For this purpose, data taken during the Convective and Orographically Induced Precipitation Study (COPS) campaign in 2007 from two vertically pointing radars—a 24.15-GHz Micro Rain Radar (MRR) and a 35.5-GHz polarimetric cloud radar—were analyzed. In this analysis a correlation between parameters of the Z–R relation and LDR peak values are revealed, implying that the LDR magnitude within the melting layer must be influenced by the size of melting particles. Furthermore, an LDR classification scheme shows an improvement of R retrieval with respect to the global Z–R relation optimized for the dataset herein. However, to asses the impact for improved QPE in the above-mentioned applications, future research is necessary.

scholarly journals Quantifying the Added Value of Snow Cover Area Observations in Passive Microwave Snow Depth Data Assimilation

2015 ◽  
Vol 16 (4) ◽  
pp. 1736-1741 ◽  
Author(s):  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Kristi R. Arsenault ◽  
Augusto Getirana ◽  
David Mocko ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Accurate Determination ◽  
Passive Microwave ◽  
Added Value ◽  
Surface Model ◽  
Snow Cover Area ◽  
The Impact

Abstract Accurate determination of snow conditions is important for several water management applications, partly because of the significant influence of snowmelt on seasonal streamflow prediction. This article examines an approach using snow cover area (SCA) observations as snow detection constraints during the assimilation of snow depth retrievals from passive microwave sensors. Two different SCA products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS)] are employed jointly with the snow depth retrievals from a variety of sensors for data assimilation in the Noah land surface model. The results indicate that the use of MODIS data is effective in obtaining added improvements (up to 6% improvement in aggregate RMSE) in snow depth fields compared to assimilating passive microwave data alone, whereas the impact of IMS data is small. The improvements in snow depth fields are also found to translate to small yet systematic improvements in streamflow estimates, especially over the western United States, the upper Missouri River, and parts of the Northeast and upper Mississippi River. This study thus demonstrates a simple approach for exploiting the information from SCA observations in data assimilation.

scholarly journals River Flood Detection Using Passive Microwave Remote Sensing in a Data-Scarce Environment: A Case Study for Two River Basins in Malawi

2021 ◽  
Vol 9 ◽  
Author(s):  
Lone C. Mokkenstorm ◽  
Marc J. C. van den Homberg ◽  
Hessel Winsemius ◽  
Andreas Persson
Keyword(s):  
Remote Sensing ◽  
River Discharge ◽  
Spatial Scales ◽  
Time Lag ◽  
Microwave Remote Sensing ◽  
River Basins ◽  
Passive Microwave ◽  
Small Scale ◽  
Passive Microwave Remote Sensing ◽  
The Impact

Detecting and forecasting riverine floods is of paramount importance for adequate disaster risk management and humanitarian response. However, this is challenging in data-scarce and ungauged river basins in developing countries. Satellite remote sensing data offers a cost-effective, low-maintenance alternative to the limited in-situ data when training, parametrizing and operating flood models. Utilizing the signal difference between a measurement (M) and a dry calibration (C) location in Passive Microwave Remote Sensing (PMRS), the resulting rcm index simulates river discharge in the measurement pixel. Whilst this has been demonstrated for several river basins, it is as of yet unknown at what ratio of the spatial scales of the river width vs. the PMRS pixel resolution it remains effective in East-Africa. This study investigates whether PMRS imagery at 37 GHz can be effectively used for flood preparedness in two small-scale basins in Malawi, the Shire and North Rukuru river basins. Two indices were studied: The m index (rcm expressed as a magnitude relative to the average flow) and a new index that uses an additional wet calibration cell: rcmc. Furthermore, the results of both indices were benchmarked against discharge estimates from the Global Flood Awareness System (GloFAS). The results show that the indices have a similar seasonality as the observed discharge. For the Shire River, rcmc had a stronger correlation with discharge (ρ = 0.548) than m (ρ = 0.476), and the former predicts discharge more accurately (R2 = 0.369) than the latter (R2 = 0.245). In Karonga, the indices performed similarly. The indices do not perform well in detecting individual flood events when comparing the signal to a flood impact database. However, these results are sensitive to the threshold used and the impact database quality. The method presented simulated Shire River discharge and detected floods more accurately than GloFAS. It therefore shows potential for river monitoring in data-scarce areas, especially for rivers of a similar or larger spatial scale than the Shire River. Upstream pixels could not directly be used to forecast floods occurring downstream in these specific basins, as the time lag between discharge peaks did not provide sufficient warning time.

scholarly journals Microwave single-scattering properties of non-spheroidal raindrops

2020 ◽  
Vol 13 (12) ◽  
pp. 6933-6944
Author(s):  
Robin Ekelund ◽  
Patrick Eriksson ◽  
Michael Kahnert
Keyword(s):  
Remote Sensing ◽  
Microwave Remote Sensing ◽  
Single Scattering ◽  
Large Set ◽  
Brightness Temperatures ◽  
Active Measurements ◽  
Back Scattering ◽  
T Matrix ◽  
Active Microwave Remote Sensing ◽  
The Impact

Abstract. Falling raindrops undergo a change in morphology as they grow in size and the fall speed increases. This change can lead to significant effects in passive and active microwave remote sensing measurements, typically in the form of a polarization signal. Because previous studies generally only considered either passive or active measurements and a limited set of frequencies, there exist no general guidelines on how and when to consider such raindrop effects in scientific and meteorological remote sensing. In an attempt to provide an overview on this topic, this study considered passive and active remote sensing simultaneously and a wider set of frequencies than in previous studies. Single-scattering property (SSP) data of horizontally oriented raindrops were calculated using the T-matrix method at a large set of frequencies (34 in total). The shapes of the raindrops were calculated assuming an aerodynamic equilibrium model, resulting in drops with flattened bases. The SSP data are published in an open-access repository in order to promote the usage of realistic microphysical assumptions in the microwave remote sensing community. Furthermore, the SSPs were employed in radiative transfer simulations of passive and active microwave rain observations, in order to investigate the impact of raindrop shape upon observations and to provide general guidelines on usage of the published database. Several instances of noticeable raindrop shape-induced effects could be identified. For instance, it was found that the flattened base of equilibrium drops can lead to an enhancement in back-scattering at 94.1 GHz of 1.5 dBZ at 10 mm h−1, and passive simulations showed that shape-induced effects on measured brightness temperatures can be at least 1 K.

L-band soil moisture retrievals using microwave based temperature and filtering. Towards model-independent climate data records

2021 ◽  
Author(s):  
Robin van der Schalie ◽  
Mendy van der Vliet ◽  
Nemesio Rodríguez-Fernández ◽  
Wouter Dorigo ◽  
Tracy Scanlon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Historical Record ◽  
Passive Microwave ◽  
Climate Data ◽  
Mean Values ◽  
Model Independent ◽  
L Band ◽  
And Function ◽  
The Impact

&lt;p&gt;The CCI Soil Moisture dataset (CCI SM, Dorigo et al., 2017) is the most extensive climate data record (CDR) of satellite soil moisture to date and is based on observations from multiple active and passive microwave satellite sensors. It provides coverage all the way back to 1978 and is updated yearly both in terms of algorithm and temporal coverage. In order to maximize its function as a CDR, both long term consistency and (model-)independence are high priorities in its development.&amp;#160;&lt;/p&gt;&lt;p&gt;Two important satellite missions integrated into the CCI SM are the ESA Soil Moisture and Ocean Salinity mission (SMOS, Kerr et al., 2010) and the NASA Soil Moisture Active Passive mission (SMAP, Entekhabi et al., 2010). These missions distinguish themselves with their unique L-band (1.4 GHz) radiometers, which are theoretically more suitable for soil moisture retrieval than the prior available higher frequencies like C- X- and Ku-band (6.9 to 18.0 GHz).&amp;#160;&lt;/p&gt;&lt;p&gt;However, these L-band missions are lacking onboard sensors for observations from higher frequencies Ku-, K- and Ka-band, which are normally used within the Land Parameter Retrieval Model (Owe et al., 2008), the baseline algorithm for passive microwave retrievals within the CCI SM, for retrieving the effective temperature (Holmes et al., 2009) and providing filters for snow/frozen conditions (Van der Vliet et al., 2020). Therefore, the retrievals from the current L-band missions make use of temperature and filters derived from global Land Surface Models (LSM, Van der Schalie et al., 2016). For a CDR that should function as an independent climate benchmark, this is a strong disadvantage.&lt;/p&gt;&lt;p&gt;Within this study the aim is to evaluate the impact of replacing the LSM based input for L-band soil moisture retrievals with one that comes from passive microwave observations. We use an inter-calibrated dataset existing of 6 different sensors that cover the complete SMOS and SMAP historical record (and further), consisting of AMSR2, AMSR-E, TRMM, GPM, Fengyun-3B and Fengyun-3D. These satellites are merged together using a minimization function that also penalizes errors in the Microwave Polarization Difference Index (MPDI) for a higher level of stability compared to using traditional linear regressions.&lt;/p&gt;&lt;p&gt;As currently the 6 am L-band retrievals are seen as the most reliable, and are currently the only ones used within the CCI, the main focus will be on the effects of using the 1:30 am observations from the inter-calibrated dataset as input. However, to make the method also applicable for daytime observations, the 6 pm retrievals have also been tested using an average of 1:30 pm and 1:30 am (next day) observations.&amp;#160;&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;This evaluation will provide an overview of the differences, giving insight on how this affects coverage, mean values, standard deviations and their inter-correlation. Secondly, we will test the resulting quality against both in situ observations and ERA5. A similar performance of this new dataset shows this is a good way to standardize input on temperature and filtering within the CCI SM, further improving its consistency and function as a model-independent CDR.&lt;/p&gt;

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