The Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its associated latent heating.

The primary objective of the 2A21 is to compute the path integrated attenuation (PIA), using the surface reference technique (SRT). The SRT relies on the assumption that the difference between the measurements of the normalized surface cross section within and outside the rain provides a measure of the PIA.

Two types of non-rain surface cross section (sigma-zero) reference estimates are used: spatial and temporal. In the spatial surface reference data set, the mean and standard deviation of the surface cross sections are calculated over a running window of Ns fields of view before rain is encountered. These operations are performed separately for each of the 49+2 incidence angles of TRMM (corresponding to the cross-track scan from -17 degrees to + 17 degrees with respect to nadir). The two additional angle bins (making the total 51 rather than 49) are to account for non-zero pitch/roll angles that can shift the incidence angle with respect to nadir outside the normal range.

For the temporal surface reference data set, the running mean and standard deviation are computed over a 1 degree x 1 degree (latitude, longitude) grid. Within each 1 degree x 1 degree grid cell, the data are further categorized into incidence angle categories (26). The number of observations in each category, Nt, are also recorded. Note that, for the temporal reference data set, no distinction is made between the port and starboard incidence angles. So, instead of 49 incidence angles, there are only 25 + 1, where the additional bin corresponds to angles greater than the normal range.

When rain is encountered, the mean and standard deviations of the reference sigma-zero values are retrieved from the spatial and temporal surface reference data sets. To determine which reference measurement is to be used, the algorithm checks whether Nt >= Ntmin and Ns >= Nsmin, where Ntmin and Nsmin are the minimum number of samples that are needed to be considered a valid reference estimate for the temporal and spatial reference data sets, respectively. (Presently, Ntmin = 50 and Nsmin = 8). If neither condition is satisfied, no estimate of the PIA is made and the flags are set accordingly. If only one condition is met, then the surface reference data which corresponds to this is used. If both conditions are satisfied, the surface reference data is taken from that set which has the smaller standard deviation.

If a valid surface reference data set exists (i.e., either Nt >= Ntmin or Ns >= Nsmin or both) then the 2-way path attenuation (PIA) is estimated from the equation:

PIA =

where sigma-zero(in rain) is the value of the surface cross section over the rain volume of interest and

To obtain information as to the reliability of this PIA estimate we consider the difference between the PIA, as derived in the above equation, and the standard deviation as calculated from the no-rain sigma-zero values and stored in the reference data set. Labeling this as std dev(reference value), then the reliability factor of the PIA estimate is obtained from:

reliabFactor = PIA - std dev(reference value)

When this quantity is large, the reliability is considered high and conversely. This is the basic...

This dataset is Version 2 of the geo-located and averaged Level 1B Sigma-0 measurements and wind retrievals from the SeaWinds on QuikSCAT platform, initiated in the months following the failure of the rotating antenna motor on 22 November 2009, using the various incidence angles at which QuikSCAT was pointed during the time period from November 2009 until present. Incidence angles were varied in order to cross-calibrate the Oceansat-2 and RapidScat scatterometers and to extend the known Ku-band geophysical model function. The averaging of the L1B input data combined with the wind vector processing results are both contained in this product referred to hereafter as Level 1C (L1C). The fixed and repointed beam processing is relative to either the one corresponding to the vertically polarized "outer" beam or the other corresponding to the horizontally polarized "inner" beam. The Sigma-0 values from the fixed operating beam for each frame are averaged to a single value representing approximately 50 samples. The data points are land flagged, collocated with ECMWF surface winds, and have climatological nadir attenuations provided for the location and time of the data (not applied to the sigma0). The following enhancements have been applied in the Version 2 re-processing: 1) the GMF has been updated (QNS2016a) to make use of ECMWF nowcast 1x1 degree resolution wind direction information for the entire historical data record; 2) the new QNS2016a GMF leverages a calibration adjustment from Remote Sensing Systems (RSS) resulting in a consistently lower Normalized Radar Cross Section (NRCS or Sigma-0) measurements that establishes a Sigma-0 bias of -0.25 dB (-5.9% linear scale) compared to the L1C Version 1 data; 3) the new QNS2016a GMF also applies an azimuthal modulation that is decreased by several tenths of a dB (for Sigma-0) in variation with wind speed; this results in a more consistent wind speed retrieval comparison between "non-spinning" and "spinning" modes of the QuikSCAT instrument; 4) spacecraft attitude was re-estimated using slice data over multiple orbits as a replacement for lost echo-tracking capability during the "non-spinning" mode of the instrument; this new attitude estimation follows an unpublished manual technique that leverages the echo power of individual slice observations; since only a small subset of slice observations are analyzed, rapid variations in attitude are not captured; 5) continues data production beyond October 2016 through the end of mission on 30 August 2018. Retrieved wind directions are only slightly different from ECMWF values and should not be considered an independent measurement of wind direction. Retrieved wind speeds do not depend significantly on ECMWF speeds as evidenced by the fact that they agree closely with WindSAT polarimetric radiometer speeds whenever WindSAT and ECMWF disagree. The Sigma0 values have also been corrected for scan loss (due to the fact that the antenna does not scan) and for X-factor changes due to repointing.

Many studies have confirmed that size distributions of framboidal pyrite can be an effective indicator of bottom-water redox conditions in modern as well as ancient sedimentary environments. However, one environment in which production of framboidal pyrite has not been sufficiently studied to date is the sulfate-methane transition zone (SMTZ), in which anaerobic oxidation of methane (AOM) is coupled with microbial sulfate reduction (MSR) to enhance iron sulfide mineral precipitation (e.g., FeS2, FeS, and Fe3S4). Here, we document for the first time size distributions for pyrite framboids from the SMTZ, based on data from two sites in the methane hydrate-bearing region of the northern South China Sea. On the basis of framboid size, pyrite concentration, and sulfur isotope data, we propose new insights into the formation process of authigenic pyrite framboids within the SMTZ. We conclude that (1) Enhanced anaerobic oxidation of methane (AOM) not only plays a dominant role in the accumulation of 34S-enriched pyrite but also is responsible for formation of highly variable and sometimes exceptionally large pyrite framboids in the SMTZ; (2) Most framboids occur in 'framboid clusters' which commonly exhibit rod-like shapes, secondary overgrowths, heavier d34S values, and unusual size distributions (e.g., mean size > 20 µm and standard deviation > 3.0 µm) in the SMTZ; (3) Pyrite framboids formed in the SMTZ, which have characteristics different from those formed in the sulfate reduction zone (SRZ), do not comment on redox conditions of the overlying water column; and (4) Framboid occurrences with similar characteristics in ancient marine deposits may be considered indicators of enhanced AOM and mark the former position of the SMTZ in the paleo-marine system. In addition, significant quantities of elemental sulfur were observed in the SMTZ, possibly related to anaerobic oxidation of hydrogen sulfide and fluctuations of the SMTZ.