Dong-Bin Shin

The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors

Abstract This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithms poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the sc...

Comparison of Freezing-Level Altitudes from the NCEP Reanalysis with TRMM Precipitation Radar Brightband Data

Abstract A global climatology of the altitude of the freezing level (0°C isotherm) is computed using 20 yr of 6-hourly output from the National Centers for Environmental Prediction (NCEP) reanalysis system. Mean statistics discussed include monthly means and climatological monthly means. Variance statistics include the standard deviation of the 6-hourly values with the month and the standard deviation of the monthly means. In the Tropics, freezing levels are highest (∼5000 m) and both intramonth and interannual variability is lowest. Freezing levels are lower and variability is higher in the subtropics and midlatitudes. In 1998 there are unusually high freezing levels in the eastern Pacific Ocean relative to the 20-yr climatology, consistent with elevated sea surface temperatures associated with the 1997–98 El Nino. Freezing levels return to near-climatological values during the last half of 1998. The individual monthly means for 1998 and the 20-yr climatology are compared with monthly means of the altitu...

Parametric Rainfall Retrieval Algorithms for Passive Microwave Radiometers

Abstract A methodology is described to construct fully parametric rainfall retrieval algorithms for a variety of passive microwave sensors that exist today and are planned for the future. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is used to retrieve nonraining geophysical parameters. The method then blends these background geophysical parameters with three-dimensional precipitation fields obtained by matching the TRMM precipitation radar (PR) reflectivity profiles with cloud-resolving model simulations to produce a consistent three-dimensional atmospheric description. Based upon this common description, radiative transfer simulations corresponding to specific microwave sensors are then employed to compute radiances from clear and rainy scenes, as might be seen by any specified microwave radiometer. Last, a Bayesian retrieval methodology is used in conjunction with this database to derive the most likely surface rainfall as well as its vertical structure. By avoiding any depende...

Constraining Microwave Brightness Temperatures by Radar Brightband Observations

Multichannel microwave sensors make it possible to construct physically based rainfall retrieval algorithms. In these schemes, errors arising from the inaccuracy of the physical modeling of the cloud system under observation have to be accounted for. The melting layer has recently been identified as a possible source of bias when stratiform events are considered. In fact, Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations reveal systematic differences in the observed brightness temperatures between similar rain profiles that often differ only by the presence or absence of a bright band. A sensitivity study of the scattering properties of the melting layer with different one-dimensional steadystate microphysical and electromagnetic models is performed. The electromagnetic modeling of the ice particle density and assumption of the ventilation coefficient parameterization is found to have the greatest impact on the extinction profiles. Data taken from a 0.915-GHz National Oceanic and Atmospheric Administration (NOAA) profiler during the Kwajalein Experiment (KWAJEX) field campaign are used to reduce the uncertainties in the modeling of the bright band. The profiler data reduce the number of viable parameterizations, which in turn leads to a reduction in the variability of the upwelling radiances (simulated at TMI angle) for different cloud simulations. Using the parameterizations that best match the profiler data, the brightness temperatures TB generally increase if mixed-phase precipitation is included in the model atmosphere. The effect is most pronounced for systems with low freezing levels, such as a midlatitude cold front simulation. For TMI footprints at 10.65 GHz, the increase in the TB from the bright band generally increases with rain rate and changes by as much as ;15‐20 K. At 19.35 GHz the maximum effect is found around 3‐5 mm h21 (;15 K), and at 37 GHz the maximum effect is around 1 mm h21 (;10 K), while at 85.5 GHz the effect is always lower than 3 K. Despite the reduction of uncertainties achieved by using 915-MHz profiler data, differences between parameterizations are still significant, especially for the higher TMI frequencies. A validation experiment is proposed to solve this issue and to further reduce the uncertainties in brightband modeling.

A Summary of Reflectivity Profiles from the First Year of TRMM Radar Data

Abstract A preliminary climatology of reflectivity profiles derived from the first spaceborne precipitation radar (PR), which is on board the Tropical Rainfall Measuring Mission (TRMM) satellite, is described using the data from January 1998 to February 1999. This study focuses on the behavior of the melting-layer (bright band) altitude in stratiform precipitation. This analysis will be useful for improving passive microwave radiometric estimations of rain rates because it provides information about otherwise unknown parameters in the estimation models (the depth of the rain column). The monthly means of the melting-layer altitude estimated over 10° × 10° latitude–longitude grid boxes show that high melting layers (>4.5 km) tend to appear during extreme events such as El Nino and the Asian summer monsoon, and lower melting layers are usually observed in the winter hemisphere, which suggests a close relationship between surface temperature and the melting-layer altitude. Detailed climatologies of the profi...

Comparison of the Monthly Precipitation Derived from the TRMM Satellite

A comparison of monthly rainfall derived from the version 5 of TRMM Microwave Imager (TMI), Precipitation Radar (PR), TRMM Combined algorithm (TCA) and TMI-emission algorithm (TMIE) using two years (1998 to 1999) of TRMM data was made. The global (TRMM domain, 40°N∼ 40°S) average rain rates are 3.29, 2.62 and 2.93 mm/day over land and 3.02, 2.47 and 2.54 mm/day over oceans for TMI, PR, and TCA respectively. The TMIE oceanic average is 2.90 mm/day. For both the global and zonal means, the TMI rain rates are the largest and the PR estimates lowest. Regression analyses show the offsets of algorithms are close to zero. According to a paired t-test, significant differences exist between TMI and PR and between TMI and TCA, especially in oceanic dry regions. However, the difference between PR and TCA was judged to be insignificant. Comparison of PR and TMIE shows that a statistically significant difference is evident in the oceanic dry regions.

Agreement between monthly precipitation estimates from TRMM satellite, NCEP reanalysis, and merged gauge-satellite analysis

[1] Global monthly precipitation is a critical element in understanding variability of the Earth’s climate including changes in the hydrological cycle associated with global warming. The NCEP reanalysis (R1), GPCP, CMAP, and TMPA precipitation data sets are often used in climate studies. This study compares the data sets (R1, GPCP, CMAP, and TMPA) with the TRMM precipitation data sets derived from the TRMM precipitation radar (TPR), microwave imager (TMI), and combined algorithm (TCA) for 11 years (1998–2008) over the satellite’s domain (40°S–40°N). The domain precipitation estimates from seven data sets range from 2.44 to 3.38 mm d −1 over the ocean and from 1.98 to 2.83 mm d −1 over land. The regional differences between the TPR and the other data sets are analyzed by a paired t test. Particularly, statistically significant differences between TPR and GPCP and between TPR and CMAP are found in most oceanic regions and in some land areas. In general, there exists substantial disagreement in precipitation intensities from the precipitation data sets. Therefore, significant consideration is given to the uncertainties in the data sets prior to applying the results to climate studies such as estimations of the global hydrological budget analyses. Meanwhile, the anomalies from all the data sets agree relatively well in their variability patterns. It is also found that the dominant mode of interannual variability which is associated with the ENSO pattern is clearly demonstrated by all precipitation data sets. These results suggest that all considered precipitation data sets may produce similar results when they are used for climate variability analyses on annual to interannual time scales.

Impacts of A Priori Databases Using Six WRF Microphysics Schemes on Passive Microwave Rainfall Retrievals

AbstractPhysically based rainfall retrievals from passive microwave sensors often make use of cloud-resolving models (CRMs) to build a priori databases of potential rain structures. Each CRM, however, has its own cloud microphysics assumptions. Hence, approximated microphysics may cause uncertainties in the a priori information resulting in inaccurate rainfall estimates. This study first builds a priori databases by combining the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations and simulations from the Weather Research and Forecasting (WRF) model with six different cloud microphysics schemes. The microphysics schemes include the Purdue–Lin (LIN), WRF Single-Moment 6 (WSM6), Goddard Cumulus Ensemble (GCE), Thompson (THOM), WRF Double-Moment 6 (WDM6), and Morrison (MORR) schemes. As expected, the characteristics of the a priori databases are inherited from the individual cloud microphysics schemes. There are several distinct differences in the databases. Particularly, excessi...

Errors Incurred in Sampling a Cyclostationary Field

Low earth-orbiting satellites such as the Tropical Rainfall Measuring Mission (TRMM) estimate month-long averages of precipitation (or other fields). A difficulty is that such a satellite sensor returns to the same spot on the planet at discrete intervals, about 11 or 12 h apart. This discrete sampling leads to a sampling error that is the one of the largest components of the error budget. Previous studies have examined this type of error for stationary random fields, but this paper examines the possibility that the field has a diurnally varying standard deviation, a property likely to occur in precipitation fields. This is a special case of the more general cyclostationary field. In this paper the authors investigate the mean square error (mse) for the monthly averaging case derived from the satellites whose revisiting intervals are 12 h (sun synchronous) and off 12 h (11.75 h). In addition, the authors take the diurnal cycle of the standard deviation to be a constant plus a single sinusoid, either diurnal or semidiurnal. The authors have derived an mse formula consisting of three parts: the errors from the stationary background, the cyclostationary part, and a cross-term between them. The separate parts of the mse allow the authors to assess the contribution of the cyclostationary error to the total mse. The results indicate that the cyclostationary errors due to the diurnal variation appear small for both a 12-h and an off-12-h (11.75 h) revisiting satellite. In addition, the cyclostationary error amounts are similar to each other. For the semidiurnally varying field, the cyclostationary errors increase rapidly as the magnitude of the variance cycle increases for both the 12-h and off-12-h revisting satellites. However, the off-12-h sampling shows the cyclostationary error to be less than that of the exact 12-h sampling. Furthermore, the authors have evaluated the cyclostationary error as a function of the phase of the satellite visit as it is shifted from the phase of the diurnal cycles (the sun-synchronous case or the start of the month for the off-12-h case). It is found that the cyclostationary error observed from the off-12-h satellite is much less sensitive to the phase shift than the cyclostationary error from the exact 12-h satellite.

The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation

The new Version 2.3 of the GPCP Monthly analysis is described in terms of changes made to improve the homogeneity of the product, especially after 2002. These changes include corrections to cross calibration of satellite data inputs and updates to the gauge analysis. Over ocean, changes starting in 2003 result in an overall precipitation increase of 1.8% after 2009. Updating the gauge analysis to its final, high quality version increases the global land total by 1.8% for the post-2002 period. These changes correct a small, incorrect dip in the estimated global precipitation over the last decade in the earlier Version 2.2. The GPCP analysis is also used to describe global precipitation for 2017. The general La Nina pattern for 2017 is noted and the evolution from the early 2016 El Nino pattern is described. The 2017 global value is one of the highest for the 19792017 period, exceeded only by 2016 and 1998 (both El Nino years) and reinforces the small positive trend. Results for 2017 also reinforce significant trends in precipitation intensity (on a monthly scale) in the tropics. These results for 2017 indicate the value of the GPCP analysis for climate monitoring in addition to research.