scholarly journals Temperature variability at Siple Dome, West Antarctica, derived from ECMWF re-analyses, SSM/I and SMMR brightness temperatures and AWS records

2002 ◽  
Vol 34 ◽  
pp. 106-112 ◽  
Author(s):  
Sarah B. Das ◽  
Richard B. Alley ◽  
David B. Reusch ◽  
Christopher A. Shuman
Keyword(s):  
Southern Oscillation ◽  
Microwave Radiometer ◽  
Temperature Variability ◽  
West Antarctica ◽  
Weather Forecasts ◽  
Brightness Temperatures ◽  
Siple Dome ◽  
Temperature Records ◽  
Microwave Imager ◽  
Temperature Time

AbstractWe produced four independent temperature time series derived from different sensors for the Siple Dome region of West Antarctica to investigate seasonal to interannual temperature variability over the last 20 years. We use data from automatic weather station air-temperature records (1997–99), European Centre for Medium-range Weather Forecasts surface temperature from the 15 year re-analyses (ERA-15, 1979–93), and emissivity-corrected brightness temperatures from the Special Sensor Microwave/Imager (1987–99) and the Scanning Multichannel Microwave Radiometer (1978–87). Each technique has limitations and errors, but all respond to temperature, and all agree in the large patterns of temperature variability over time. Our results show that there is high seasonal to interannual variability in both mean temperature and variance in the Siple Dome region during the study period. In particular, fluctuations in seasonal to interannual temperature variance occur on an approximately 5 year cycle and correlate with variations in the Southern Oscillation Index.

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

scholarly journals First Three Years of the Microwave Radiometer aboard Envisat: In-Flight Calibration, Processing, and Validation of the Geophysical Products

2006 ◽  
Vol 23 (6) ◽  
pp. 802-814 ◽  
Author(s):  
E. Obligis ◽  
L. Eymard ◽  
N. Tran ◽  
S. Labroue ◽  
P. Femenias
Keyword(s):  
Microwave Radiometer ◽  
Radiative Transfer Model ◽  
Altimeter Data ◽  
Transfer Model ◽  
Path Delay ◽  
Inversion Algorithm ◽  
Weather Forecasts ◽  
Brightness Temperatures ◽  
Medium Range ◽  
Satellite Altimeter Data

Abstract The Envisat microwave radiometer is designed to correct the satellite altimeter data for the excess path delay resulting from tropospheric humidity. Neural networks have been used to formulate the inversion algorithm to retrieve this quantity from the measured brightness temperatures. The learning database has been built with European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and simulated brightness temperatures by a radiative transfer model. The in-flight calibration has been performed in a consistent way by adjusting measurements on simulated brightness temperatures. Finally, coincident radiosonde measurements are used to validate the Envisat wet-tropospheric correction, and this comparison shows the good performances of the method.

scholarly journals Snowmelt onset over Arctic sea ice from passive microwave satellite data: 1979–2012

2014 ◽  
Vol 8 (6) ◽  
pp. 2089-2100 ◽  
Author(s):  
A. C. Bliss ◽  
M. R. Anderson
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Data Set ◽  
Brightness Temperatures ◽  
Arctic Sea ◽  
Microwave Imager ◽  
The Arctic Region

Abstract. An updated version (Version 3) of the Snow Melt Onset Over Arctic Sea Ice from SMMR (Scanning Multichannel Microwave Radiometer) and SSM/I-SSMIS (Special Sensor Microwave/Imager-Special Sensor Microwave Imager/Sounder) Brightness Temperatures data set is now available. The data record has been reprocessed and extended to cover the years 1979–2012. From this data set, a statistical summary of melt onset (MO) dates on Arctic sea ice is presented. The mean MO date for the Arctic Region is 13 May (132.5 DOY – day of year) with a standard deviation of ±7.3 days. Regionally, mean MO dates vary from 15 March (73.2 DOY) in the St. Lawrence Gulf to 10 June (160.9 DOY) in the Central Arctic. Statistically significant decadal trends indicate that MO is occurring 6.6 days decade−1 earlier in the year for the Arctic Region. Regionally, MO trends are as great as −11.8 days decade−1 in the East Siberian Sea. The Bering Sea is an outlier and MO is occurring 3.1 days decade−1 later in the year.

Determination of an Amazon Hot Reference Target for the On-Orbit Calibration of Microwave Radiometers

2005 ◽  
Vol 22 (9) ◽  
pp. 1340-1352 ◽  
Author(s):  
Shannon T. Brown ◽  
Christopher S. Ruf
Keyword(s):  
Empirical Formula ◽  
Incidence Angle ◽  
Microwave Radiometer ◽  
Time Of Day ◽  
Angle Of Incidence ◽  
Physically Based Model ◽  
Brightness Temperatures ◽  
Amazon Rain Forest ◽  
Physically Based ◽  
Microwave Imager

Abstract A physically based model is developed to determine hot calibration reference brightness temperatures (TBs) over depolarized regions in the Amazon rain forest. The model can be used to evaluate the end-to-end calibration of any satellite microwave radiometer operating at a frequency between 18 and 40 GHz and angle of incidence between nadir and 55°. The model is constrained by Special Sensor Microwave Imager (SSM/I) TBs measured at 19.35, 22.2, and 37.0 GHz at a 53° angle of incidence and extrapolates/interpolates those measurements to other frequencies and incidence angles. The rms uncertainty in the physically based model is estimated to be 0.57 K. For instances in which coincident SSM/I measurements are not available, an empirical formula has been fit to the physical model to provide hot reference brightness temperature as a function of frequency, incidence angle, time of day, and day of year. The empirical formula has a 0.1-K rms deviation from the physically based model for annual averaged measurements and at most a 0.6-K deviation from the model for any specific time of day or day of year.

scholarly journals Siple Dome shallow ice cores: a study in coastal dome microclimatology

2014 ◽  
Vol 10 (3) ◽  
pp. 1253-1267 ◽  
Author(s):  
T. R. Jones ◽  
J. W. C. White ◽  
T. Popp
Keyword(s):  
Southern Oscillation ◽  
Ice Cores ◽  
Southern Annular Mode ◽  
Snow Accumulation ◽  
West Antarctica ◽  
Spatial Gradients ◽  
West Antarctic ◽  
The Pacific ◽  
Siple Dome ◽  
Borehole Temperatures

Abstract. Ice cores at Siple Dome, West Antarctica, receive the majority of their precipitation from Pacific Ocean moisture sources. Pacific climate patterns, particularly the El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode (SAM), affect local temperature, atmospheric circulation, snow accumulation, and water isotope signals at Siple Dome. We examine borehole temperatures, accumulation, and water isotopes from a number of shallow ice cores recovered from a 60 km north–south transect of the dome. The data reveal spatial gradients partly explained by orographic uplift, as well as microclimate effects that are expressed differently on the Pacific and inland flanks. Our analyses suggest that while an ENSO and SAM signal are evident at Siple Dome, differences in microclimate and possible postdepositional movement of snow makes climate reconstruction problematic, a conclusion which should be considered at other West Antarctic coastal dome locations.

scholarly journals Strato-mesospheric carbon monoxide profiles above Kiruna since 2008

2016 ◽  
Author(s):  
Niall J. Ryan ◽  
Mathias Palm ◽  
Uwe Raffalski ◽  
Richard Larsson ◽  
Gloria Manney ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Microwave Radiometer ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Atmospheric Temperature ◽  
Weather Forecasts ◽  
Us Air Force ◽  
Microwave Imager ◽  
High Level ◽  
Co Concentrations

Abstract. This paper presents the retrieval and validation of a self-consistent timeseries of carbon monoxide (CO) above Kiruna using measurements from the Kiruna Microwave Radiometer (KIMRA). The spectra are inverted using an optimal estimation method to retrieve altitude profiles of CO concentrations in the atmosphere within approximately 48–84 km altitude. Atmospheric temperature data from the Special Sensor Microwave Imager/Sounder aboard the US Air Force meteorological satellite, DMSP-F18, are used in the inversion of KIMRA spectra between January 2011 and May 2014. This dataset is compared with CO data from Microwave Limb Sounder aboard the Aura satellite and shows a high level of agreement at all altitudes: There is a maximum bias for KIMRA of ~ 0.65 ppm at 68 km (corresponding to 14.7 % of the mean CO value at 68 km), and correlations between the instruments are within 0.87 and 0.94. To expand the CO dataset outside of the lifetime of DMSP-F18, another inversion setup was used that incorporates modelled temperatures from the European Centre for Medium-Range Weather Forecasts. The effect on the retrieved CO profiles when using a different temperature dataset in the inversion was assessed. A comparison of the two overlapping KIMRA CO datasets shows a bias of  0.98 at all altitudes below 82.5 km. The extended dataset shows a higher variation (≤ 6 %) in CO concentrations that is not explained by random error estimates. The extended KIMRA CO timeseries currently spans 2008 to 2015, with gaps corresponding to non-operation and summer periods when CO concentrations below ~ 90 km drop to very low values. The data can be accessed at: https://doi.pangaea.de/10.1594/PANGAEA.861730.

scholarly journals Intercalibration of the GPM Microwave Radiometer Constellation

2016 ◽  
Vol 33 (12) ◽  
pp. 2639-2654 ◽  
Author(s):  
Wesley Berg ◽  
Stephen Bilanow ◽  
Ruiyao Chen ◽  
Saswati Datta ◽  
David Draper ◽  
...  
Keyword(s):  
Microwave Radiometer ◽  
Advanced Technology ◽  
Satellite Mission ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Intersatellite Calibration ◽  
Microwave Imager ◽  
Global Precipitation Measurement ◽  
Cross Track ◽  
Global Precipitation

AbstractThe Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration differences relative to GMI are generally within 2–3 K for channels below 92 GHz, although AMSR2 exhibits larger differences that vary with scene temperature. SSMIS calibration differences also vary with scene temperature but to a lesser degree. For SSMIS channels above 150 GHz, the differences are generally within ~2 K with the exception of SSMIS on board DMSP F19, which ranges from 7 to 11 K colder than GMI depending on frequency. The calibrations of the cross-track radiometers agree very well with GMI with values mostly within 0.5 K for the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) and the Microwave Humidity Sounder (MHS) sensors, and within 1 K for the Advanced Technology Microwave Sounder (ATMS).

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Dimensional Structure ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

scholarly journals Relationships between Southeast Australian Temperature Anomalies and Large-Scale Climate Drivers

2014 ◽  
Vol 27 (4) ◽  
pp. 1395-1412 ◽  
Author(s):  
Alexandre O. Fierro ◽  
Lance M. Leslie
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Power Spectra ◽  
Warm Season ◽  
Principal Component ◽  
Temperature Variability ◽  
Maximum Temperature ◽  
Past Century ◽  
Tasman Sea ◽  
Australian Continent

Abstract Over the past century, particularly after the 1960s, observations of mean maximum temperatures reveal an increasing trend over the southeastern quadrant of the Australian continent. Correlation analysis of seasonally averaged mean maximum temperature anomaly data for the period 1958–2012 is carried out for a representative group of 10 stations in southeast Australia (SEAUS). For the warm season (November–April) there is a positive relationship with the El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) and an inverse relationship with the Antarctic Oscillation (AAO) for most stations. For the cool season (May–October), most stations exhibit similar relationships with the AAO, positive correlations with the dipole mode index (DMI), and marginal inverse relationships with the Southern Oscillation index (SOI) and the PDO. However, for both seasons, the blocking index (BI, as defined by M. Pook and T. Gibson) in the Tasman Sea (160°E) clearly is the dominant climate mode affecting maximum temperature variability in SEAUS with negative correlations in the range from r = −0.30 to −0.65. These strong negative correlations arise from the usual definition of BI, which is positive when blocking high pressure systems occur over the Tasman Sea (near 45°S, 160°E), favoring the advection of modified cooler, higher-latitude maritime air over SEAUS. A point-by-point correlation with global sea surface temperatures (SSTs), principal component analysis, and wavelet power spectra support the relationships with ENSO and DMI. Notably, the analysis reveals that the maximum temperature variability of one group of stations is explained primarily by local factors (warmer near-coastal SSTs), rather than teleconnections with large-scale drivers.

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