scholarly journals Validation of merged MSU4 and AMSU9 temperature climate records with a new 2002–2012 vertically resolved temperature record

2015 ◽  
Vol 8 (1) ◽  
pp. 235-267 ◽  
Author(s):  
A. A. Penckwitt ◽  
G. E. Bodeker ◽  
P. Stoll ◽  
J. Lewis ◽  
T. von Clarmann ◽  
...  
Keyword(s):  
Time Series ◽  
Radio Occultation ◽  
Temperature Data ◽  
Data Sets ◽  
Data Set ◽  
Upper Troposphere ◽  
Microwave Sounding Unit ◽  
Source Data ◽  
Microwave Sounding ◽  
Temperature Time

Abstract. A new database of monthly mean zonal mean (5° zones) temperature time series spanning 17 pressure levels from 300 to 7 hPa and extending from 2002 to 2012 was created by merging monthly mean time series from two satellite-based mid-infrared spectrometers (ACE-FTS and MIPAS), a microwave sounder (SMR), and from three satellite-based radio occultation experiments (GRACE, CHAMP, and TSX). The primary intended use of this new temperature data set is to validate the merging of the Microwave Sounding Unit channel 4 (MSU4), and Advanced Microwave Sounding Unit channel 9 (AMSU9) temperature time series conducted in previous studies. The six source data sets were merged by removing offsets and trends between the different measurement series. Weighted means were calculated of the six source data sets where the weights were a function of the uncertainty on the original monthly mean data. This new temperature data set of the upper troposphere and stratosphere has been validated by comparing it to RATPAC-A, COSMIC radio occultation data as well as the NCEPCFSR reanalyses. Differences in all three cases were typically < 2 K in the upper troposphere and lower stratosphere, but could reach up to 5 K in the mid-stratosphere. The data across the 17 pressure levels have then been vertically integrated, using the MSU4/AMSU9 weighting function, to provide a deep vertical layer temperature proxy of the merged MSU4+AMSU9 series. Differences between this vertically integrated data set and two different versions of the MSU4+AMSU9 data set – one from Remote Sensing Systems and one from the University of Alabama at Huntsville – were examined for discontinuities. No statistically significant discontinuities were found in either of those two data sets suggesting that the transition from the MSU4+AMSU9 data to AMSU9 data only does not introduce any discontinuities in the MSU4+AMSU9 climate data records that might compromise their use in temperature trend analyses.

scholarly journals A method for merging nadir-sounding climate records, with an application to the global-mean stratospheric temperature data sets from SSU and AMSU

2015 ◽  
Vol 15 (7) ◽  
pp. 10085-10122 ◽  
Author(s):  
C. McLandress ◽  
T. G. Shepherd ◽  
A. I. Jonsson ◽  
T. von Clarmann ◽  
B. Funke
Keyword(s):  
Climate Model ◽  
Michelson Interferometer ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
Stratospheric Temperature ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Climate Model Simulations ◽  
Global Mean

Abstract. A method is proposed for merging different nadir-sounding climate data records using measurements from high resolution limb sounders to provide a transfer function between the different nadir measurements. The nadir-sounding records need not be overlapping so long as the limb-sounding record bridges between them. The method is applied to global mean stratospheric temperatures from the NOAA Climate Data Records based on the Stratospheric Sounding Unit (SSU) and the Advanced Microwave Sounding Unit-A (AMSU), extending the SSU record forward in time to yield a continuous data set from 1979 to present. SSU and AMSU are bridged using temperature measurements from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is of high enough vertical resolution to accurately represent the weighting functions of both SSU and AMSU. For this application, a purely statistical approach is not viable since the different nadir channels are not sufficiently linearly independent, statistically speaking. The extended SSU global-mean data set is in good agreement with temperatures from the Microwave Limb Sounder (MLS) on the Aura satellite, with both exhibiting a cooling trend of ~ 0.6 ± 0.3 K decade−1 in the upper stratosphere from 2004–2012. The extended SSU data set also compares well with chemistry-climate model simulations over its entire record, including the contrast between the weak cooling seen over 1995–2004 compared with the large cooling seen in the period 1986–1995 of strong ozone depletion.

scholarly journals The CAMELS data set: catchment attributes and meteorology for large-sample studies

2017 ◽  
Vol 21 (10) ◽  
pp. 5293-5313 ◽  
Author(s):  
Nans Addor ◽  
Andrew J. Newman ◽  
Naoki Mizukami ◽  
Martyn P. Clark
Keyword(s):  
Time Series ◽  
Data Sets ◽  
Catchment Scale ◽  
Data Set ◽  
Meteorological Forcing ◽  
Large Sample ◽  
Source Data ◽  
Daily Time Series ◽  
Comparative Hydrology ◽  
Daily Time

Abstract. We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al. (2015b, https://doi.org/10.5065/D6MW2F4D) together with the catchment attributes introduced in this paper (https://doi.org/10.5065/D6G73C3Q) constitute the freely available CAMELS data set, which stands for Catchment Attributes and MEteorology for Large-sample Studies.

scholarly journals Strato-mesospheric carbon monoxide profiles above Kiruna, Sweden (67.8 ° N, 20.4 ° E), since 2008

2017 ◽  
Vol 9 (1) ◽  
pp. 77-89 ◽  
Author(s):  
Niall J. Ryan ◽  
Mathias Palm ◽  
Uwe Raffalski ◽  
Richard Larsson ◽  
Gloria Manney ◽  
...  
Keyword(s):  
Time Series ◽  
Carbon Monoxide ◽  
Microwave Radiometer ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Atmospheric Temperature ◽  
Temperature Data ◽  
Data Sets ◽  
Data Set ◽  
Co Concentrations

Abstract. This paper presents the retrieval and validation of a self-consistent time series of carbon monoxide (CO) above Kiruna using measurements from the Kiruna Microwave Radiometer (KIMRA). The data set currently spans the years 2008–2015, and measurements are ongoing at Kiruna. The spectra are inverted using an optimal estimation method to retrieve altitude profiles of CO concentrations in the atmosphere within an average altitude range of 48–84 km. 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 KIMRA CO data set is compared with CO data from the Microwave Limb Sounder aboard the Aura satellite: there is a maximum bias for KIMRA of  ∼  0.65 ppmv at 68 km (corresponding to 14.7 % of the mean CO value at 68 km) and a maximum relative bias of 22 % (0.44 ppmv) at 60 km. Standard deviations of the differences between profiles are similar in magnitude to the estimated uncertainties in the profiles. Correlations between the instruments are within 0.87 and 0.94. These numbers indicate agreement between the instruments. To expand the CO data set 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 data set in the inversion was assessed. A comparison of the two overlapping KIMRA CO data sets shows a positive bias of  <  5 % in the extended data set and a correlation  >  0.98 between the lower retrievable altitude limit and 82.5 km. The extended data set shows a larger range ( ≤  6 %) of CO concentrations that is not explained by random error estimates. Measurements are continuing and the extended KIMRA CO time series currently spans 2008–2015, 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 doi:10.1594/PANGAEA.861730.

scholarly journals Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592092800
Author(s):  
Erin M. Buchanan ◽  
Sarah E. Crain ◽  
Ari L. Cunningham ◽  
Hannah R. Johnson ◽  
Hannah Stash ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sharing ◽  
Search Engine ◽  
Web Applications ◽  
Data Sets ◽  
Data Dictionary ◽  
Data Set ◽  
Entire Process ◽  
Shared Data ◽  
Source Data

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets’ contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.

scholarly journals Loi and Gong Low-Degree Rotational Splittings

1998 ◽  
Vol 185 ◽  
pp. 167-168
Author(s):  
T. Appourchaux ◽  
M.C. Rabello-Soares ◽  
L. Gizon
Keyword(s):  
Time Series ◽  
The Other ◽  
Data Sets ◽  
Data Set ◽  
Low Degree ◽  
Fourier Spectra

Two different data sets have been used to derive low-degree rotational splittings. One data set comes from the Luminosity Oscillations Imager of VIRGO on board SOHO; the observation starts on 27 March 96 and ends on 26 March 97, and are made of intensity time series of 12 pixels (Appourchaux et al, 1997, Sol. Phys., 170, 27). The other data set was kindly made available by the GONG project; the observation starts on 26 August 1995 and ends on 21 August 1996, and are made of complex Fourier spectra of velocity time series for l = 0 − 9. For the GONG data, the contamination of l = 1 from the spatial aliases of l = 6 and l = 9 required some cleaning. To achieve this, we applied the inverse of the leakage matrix of l = 1, 6 and 9 to the original Fourier spectra of the same degrees; cleaning of all 3 degrees was achieved simultaneously (Appourchaux and Gizon, 1997, these proceedings).

scholarly journals Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate

2014 ◽  
Vol 31 (10) ◽  
pp. 2206-2222 ◽  
Author(s):  
Xiaolei Zou ◽  
Fuzhong Weng ◽  
H. Yang
Keyword(s):  
Time Series ◽  
Atmospheric Temperature ◽  
Advanced Technology ◽  
Central Frequency ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Cross Calibration ◽  
Term Monitoring

Abstract The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A fields of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.

scholarly journals An assessment of Bayesian bias estimator for numerical weather prediction

2008 ◽  
Vol 15 (6) ◽  
pp. 1013-1022 ◽  
Author(s):  
J. Son ◽  
D. Hou ◽  
Z. Toth
Keyword(s):  
Time Series ◽  
Numerical Weather Prediction ◽  
Sampling Error ◽  
Weather Prediction ◽  
Training Data ◽  
Statistical Characteristics ◽  
Forecast Errors ◽  
Data Sets ◽  
Data Set ◽  
Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

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