Differences in the Diurnal Variation of Precipitation Estimated by Spaceborne Radar, Passive Microwave Radiometer, and IMERG

2021 ◽  
Author(s):  
Lindsey Hayden ◽  
Chuntao Liu
Keyword(s):  
Diurnal Variation ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Spaceborne Radar

scholarly journals Simulating Passive Microwave Radiometer Designs Using Simulink

SIMULATION ◽  
2002 ◽  
Vol 78 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Derek M. Burrage ◽  
Mark A. Goodberlet ◽  
Malcolm L. Heron
Keyword(s):  
Microwave Radiometer ◽  
Passive Microwave

scholarly journals Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

2016 ◽  
Author(s):  
Libo Wang ◽  
Peter Toose ◽  
Ross Brown ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Microwave Radiometer ◽  
Surface Air Temperature ◽  
Arctic Region ◽  
Temporal Variations ◽  
Passive Microwave ◽  
The Arctic ◽  
Winter Period ◽  
Strongly Correlated ◽  
Air Temperatures

Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snowpit surveys, and a surface-based passive microwave radiometer. The results of running the algorithm over the pan-Arctic region (north of 50º N) for the 1988–2013 period show that winter melt days are relatively rare averaging less than 7 melt days per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g. central Quebec and Labrador, southern Alaska, and Scandinavia). The observed spatial pattern was similar to winter melt events inferred with surface air temperatures from ERA-interim and MERRA reanalysis datasets. There was little evidence of trends in winter melt frequency except decreases over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false increasing trends from shifts in the timing of the snow cover season.

Large area mapping of soil moisture using the ESTAR passive microwave radiometer in Washita'92

1995 ◽  
Vol 54 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Thomas J. Jackson ◽  
David M. Le Vine ◽  
Calvin T. Swift ◽  
Thomas J. Schmugge ◽  
Frank R. Schiebe
Keyword(s):  
Soil Moisture ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Large Area

Sea–ice concentration at 1 km resolution in summer from merged visible and microwave radiometer observations

2021 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen
Keyword(s):  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Statistical Consistency ◽  
Microwave Data ◽  
Infrared Data

<p>Sea–ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean–atmosphere heat exchange. Satellite–based remote sensing offers the possibility for large–scale monitoring of the sea–ice concentration. Using passive microwave measurements, it is possible to observe the sea–ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m–1 km, but need clear–sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal–infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea–ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea–ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year–long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.</p>

scholarly journals Snow conditions and hydrology of the upper Colorado River basin from satellite passive microwave observations

1993 ◽  
Vol 17 ◽  
pp. 322-326 ◽  
Author(s):  
Edward G. Josberger ◽  
William J. Campbell ◽  
Per Gloersen ◽  
Alfred T.C. Chang ◽  
Al Rango
Keyword(s):  
River Basin ◽  
Colorado River ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Colorado River Basin ◽  
Small Scale ◽  
Upper Colorado River Basin ◽  
Average Maximum ◽  
Elevation Data ◽  
Snowpack Properties

Satellite passive microwave observations can provide unique mesoscale (25 km) information on snowpack properties; however, the mountainous terrain of the upper Colorado River basin compounds the difficulty of the problem. Nevertheless, observations of this region from the Scanning Multichannel Microwave Radiometer (SMMR) have provided unique, synoptic, mesoscale snowpack information from 1979 to 1987 on the snowpack extent. For this nine-year period, the SMMR 18 and 37 GHz brightness temperature observations, combined to form a parameter called NGR, show the average maximum snowpack extent covers 70% of the basin and occurs on water year day 130 (mid-February). The minimum snowpack extent took place in 1981 and covered 35% of the basin. The maximum snowpack extent took place in 1979 and covered 99% of the basin. Summation of the NGR values from each SMMR mesoscale pixel within the basin provides an index of the regional snowpack properties on both an intra- and inter-annual basis and exhibits behavior similar to the snowpack extent. When compared to the nine-year average, 1981 is the minimum year and 1979 is the maximum year. Furthermore, the sum over the basin of the annual maximum NGR from each pixel correlates with the annual discharge,r= 0.6. This correlation increases to 0.8 when digital elevation data are used to characterize each SMMR pixel and only the April through July discharge is used in the regression. Hence, this study combines the small scale elevation data with the mesoscale SMMR observations to investigate the basin-wide or regional snowpack characteristics and its hydrology.

MCMC-Based Assessment of the Error Characteristics of a Surface-Based Combined Radar–Passive Microwave Cloud Property Retrieval

2014 ◽  
Vol 53 (8) ◽  
pp. 2034-2057 ◽  
Author(s):  
Derek J. Posselt ◽  
Gerald G. Mace
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Doppler Velocity ◽  
Error Statistics ◽  
Retrieval Error ◽  
Cloud Property ◽  
Error Characteristics

AbstractCollocated active and passive remote sensing measurements collected at U.S. Department of Energy Atmospheric Radiation Measurement Program sites enable simultaneous retrieval of cloud and precipitation properties and air motion. Previous studies indicate the parameters of a bimodal cloud particle size distribution can be effectively constrained using a combination of passive microwave radiometer and radar observations; however, aspects of the particle size distribution and particle shape are typically assumed to be known. In addition, many retrievals assume the observation and retrieval error statistics have Gaussian distributions and use least squares minimization techniques to find a solution. In truth, the retrieval error characteristics are largely unknown. Markov chain Monte Carlo (MCMC) methods can be used to produce a robust estimate of the probability distribution of a retrieved quantity that is nonlinearly related to the measurements and that has non-Gaussian error statistics. In this work, an MCMC algorithm is used to explore the error characteristics of cloud property retrievals from surface-based W-band radar and low-frequency microwave radiometer observations for a case of orographic snowfall. In this particular case, it is found that a combination of passive microwave radiometer measurements with radar reflectivity and Doppler velocity is sufficient to constrain the liquid and ice particle size distributions, but only if the width parameter of the assumed gamma particle size distribution and mass–dimensional relationships are specified. If the width parameter and mass–dimensional relationships are allowed to vary realistically, a unique retrieval of the liquid and ice particle size distribution for this orographic snowfall case is rendered far more problematic.

Land Contamination Correction for Passive Microwave Radiometer Data: Demonstration of Wind Retrieval in the Great Lakes Using SSM/I

2014 ◽  
Vol 31 (10) ◽  
pp. 2094-2113 ◽  
Author(s):  
John Xun Yang ◽  
Darren S. Mckague ◽  
Christopher S. Ruf
Keyword(s):  
Great Lakes ◽  
Microwave Radiometer ◽  
Simulated Data ◽  
Passive Microwave ◽  
Current Standard ◽  
Wind Retrieval ◽  
Microwave Imager ◽  
Wind Retrievals ◽  
Land Water ◽  
Buoy Measurements

Abstract Passive microwave radiometer data over the ocean have been widely used, but data near coastlines or over lakes often cannot be used because of the large footprint with mixed signals from both land and water. For example, current standard Special Sensor Microwave Imager (SSM/I) products, including wind, water vapor, and precipitation, are typically unavailable within about 100 km of any coastline. This paper presents methods of correcting land-contaminated radiometer data in order to extract the coastal information. The land contamination signals are estimated, and then removed, using a representative antenna pattern convolved with a high-resolution land–water mask. This method is demonstrated using SSM/I data over the Great Lakes and validated with simulated data and buoy measurements. The land contamination is significantly reduced, and the wind speed retrieval is improved. This method is not restricted to SSM/I and wind retrievals alone; it can be applied more generally to microwave radiometer measurements in coastal regions for other retrieval purposes.

scholarly journals Studying turbulence by remote sensing systems during slope-2016 campaign

2018 ◽  
Vol 176 ◽  
pp. 06010
Author(s):  
Gregori de A. Moreira ◽  
Juan L. Guerrero-Rascado ◽  
Jose A. Benavent-Oltra ◽  
Pablo Ortiz-Amezcua ◽  
Roberto Róman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Doppler Lidar ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Lowermost Part

The Planetary Boundary Layer (PBL) is the lowermost part of the troposphere. In this work, we analysed some high order moments and PBL height detected continuously by three remote sensing systems: an elastic lidar, a Doppler lidar and a passive Microwave Radiometer, during the SLOPE-2016 campaign, which was held in Granada from May to August 2016. This study confirms the feasibility of these systems for the characterization of the PBL, helping us to justify and understand its behaviour along the day.

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