Tropical Rainfall Measuring Mission Era Research Articles

Interannual variations in spring lightning activity and convective rainfall over South China during the TRMM era

The interannual variability of spring lightning activity over South China (SC) and its driving mechanism are examined using the Tropical Rainfall Measuring Mission (TRMM) satellite-estimated convective rain rate and merged lightning flash rate datasets together with atmospheric and oceanic reanalysis products over the TRMM period (1998–2014). SC is climatologically characterized by high lightning flash densities, strong convective rainfall, and large convective available potential energy (CAPE) during spring. The spring SC lightning activity (SCLA) has a strong year-to-year variability, and the standard deviation of flash rates shows a spatially coherent annular distribution centered over the Pearl River Delta. The interannual variations of spring SCLA are not only highly correlated with those of convective rainfall and CAPE, but also significantly correlated with El Nino–Southern Oscillation (ENSO). The anomalously frequent SCLA tends to follow El Nino events, while infrequent SCLA preferentially follows La Nina events. Composite analyses demonstrate that during spring for the more frequent SCLA cases, the upper tropospheric atmospheric response to El Nino-related sea surface temperature anomaly (SSTA) patterns leads to strong southwesterlies over SC in conjunction with tropical westerlies over the tropical Indian Ocean, forming a divergent environment over SC. The upper-level divergence is coupled with low-level moisture convergence, generating strong mid-tropospheric updrafts to lift more air parcels to reach their level of free convection, thereby producing stronger convection with frequent SCLA. In contrast, the anomalous circulation forced by La Nina-related SSTA patterns is conducive to suppressed convection over SC and infrequent SCLA.

A Precipitation-Based Index for Tropical Intraseasonal Oscillations

AbstractCharacteristic patterns of precipitation-associated tropical intraseasonal oscillations, including the Madden–Julian oscillation (MJO) and boreal summer intraseasonal oscillation (BSISO), are identified using local empirical orthogonal function (EOF) analysis of the Tropical Rainfall Measuring Mission (TRMM) precipitation data as a function of the day of the year. The explained variances of the EOF analysis show two peaks across the year: one in the middle of the boreal winter corresponding to the MJO and the other in the middle of summer corresponding to the BSISO. Comparing the fractional variance indicates that the BSISO is more coherent than the MJO during the TRMM period. Similar EOF analyses with the outgoing longwave radiation (OLR) confirm this result and indicate that the BSISO is less coherent before the TRMM era (1979–98). In contrast, the MJO exhibits much less decadal variability. A precipitation-based index for tropical intraseasonal oscillation (PII) is derived by projecting bandpass-filtered precipitation anomalies to the two leading EOFs as a function of day of the year. A real-time version that approximates the PII is further developed using precipitation anomalies without any bandpass filtering. It is further shown that this real-time PII index may be used to diagnose precipitation in the subseasonal forecasts.

Assessment of GPM and TRMM Precipitation Products over Singapore

The evaluation of satellite precipitation products (SPPs) at regional and local scales is essential in improving satellite-based algorithms and sensors, as well as in providing valuable guidance when choosing alternative precipitation data for the local community. The Tropical Rainfall Measuring Mission (TRMM) has made significant contributions to the development of various SPPs since its launch in 1997. The Global Precipitation Measurement (GPM) mission launched in 2014 and is expected to continue the success of TRMM. During the transition from the TRMM era to the GPM era, it is necessary to assess GPM products and make comparisons with TRMM products in different regions to achieve a global view of the performance of GPM products. To this end, this study aims to assess the capability of the latest Integrated Multi-satellite Retrievals for GPM (IMERG) and two TRMM Multisatellite Precipitation Analysis (TMPA) products (TMPA 3B42 and TMPA 3B42RT) in estimating precipitation over Singapore that represents a typical tropical region. The evaluation was conducted at daily, monthly, seasonal and annual scales from 1 April 2014 to 31 January 2016. The capability of SPPs in detecting rainy/non-rainy days and different precipitation classes was also evaluated. The findings showed that: (1) all SPPs correlated well with measurements from gauges at the monthly scale, but moderately at the daily scale; (2) SPPs performed better in the northeast monsoon season (1 December–15 March) than in the inter-monsoon 1 (16 March–31 May), southwest monsoon (1 June–30 September) and inter-monsoon 2 (1 October–30 November) seasons; (3) IMERG had better performance in the characterization of spatial precipitation variability and precipitation detection capability compared to the TMPA products; (4) for the daily precipitation estimates, IMERG had the lowest systematic bias, followed by 3B42 and 3B42RT; and (5) most of the SPPs overestimated moderate precipitation events (1–20 mm/day), while underestimating light (0.1–1 mm/day) and heavy (>20 mm/day) precipitation events. Overall, IMERG is superior but with only slight improvement compared to the TMPA products over Singapore. This study is one of the earliest assessments of IMERG and a comparison of it with TMPA products in Singapore. Our findings were compared with existing studies conducted in other regions, and some limitations of the IMERG and TMPA products in this tropical region were identified and discussed. This study provides an added value to the understanding of the global performance of the IMERG product.

Comparison of TRMM-based flood indices for Gaziantep, Turkey

Floods are the most common natural disasters threatening the welfare of humanity. Gaziantep, a city located in a semi-arid region of Turkey, is occasionally flooded, and in May 2014, a flood not only caused property damage, but also resulted in the death of a lady who became trapped in flood waters. The fatality and property damage of flash floods arise from the limited response time for remediation. Despite improvements in numerical weather predictions, forecasting flash floods is not easy. Due to its frequent observations, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) real-time (RT) 3B42RT data are tested for Gaziantep flood predictions in this study. During TRMM era, six floods occurred in Gaziantep. Three-hourly 3B42RT data covering the 2000- to 2014-year period indicated high rain rates during months in which floods were observed. Also daily variation of rainfall was well represented. High-intensity rain (HIR), cumulative distribution functions (CDF) and Gaziantep Flood Index (GAFI) indices are developed for flood characterization. HIR, calculated as 10 mm/h, detected October and December of 2010 floods. CDFs with 99, 98.5, 95 and 91.3% indicated 4 floods occurred in August 2005, June 2007, October 2010 and December 2010, respectively. GAFI was able to detect 4 out of 6 occurrences (August 2005, June 2007, October 2010 and December 2010) as values ranging from 1 to 2.63 are selected for monthly precipitation. In the missed occurrence, 3B42RT did not indicate any rainfall. Although only rain rates are used in flood characterization, the results are promising, and the simplicity of the methodology favors its usage. Also, methodology can easily be implemented to TRMM following missions such as Global Precipitation Measurement Mission.

First-Year Evaluation of GPM Rainfall over the Netherlands: IMERG Day 1 Final Run (V03D)

Abstract The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

Maritime Continent rainfall variability during the TRMM era: The role of monsoon, topography and El Niño Modoki

Rainfall is among the most important climatic elements of the Maritime Continent. The Maritime Continent rainfall climate is uniquely located in the world’s most active convective area. Satellite data measured by the Tropical Rainfall Measuring Mission (TRMM) 3B43 based high-resolution rainfall products represent monthly Maritime Continent rainfall characteristics over 16 years. Several statistical scores were employed to analyse annual means, linear trends, seasonal means, and anomalous Maritime Continent rainfall characteristic percentages. The effects of land and topography on rainfall quantities were also studied and compared with the Global Precipitation Climatology Project (GPCP) gridded precipitation estimates which has low-resolution. Comparison also applied on linear correlation and partial correlation techniques to determine the relationship between rainfall and the El Niño Modoki and El Niño–Southern Oscillation (ENSO; hereafter conventional El Niño). The results show that north-south Maritime Continent precipitation is associated with and generated by the northwest and southeast monsoon patterns. In addition, the large-scale circulations are linked with heavy rainfall over this land-ocean region due to large-scale island-topography-induced convective organization. The rainfall responses to El Niño Modoki and conventional El Niño clearly indicated the times at which the conventional El Niño had a higher impact than El Niño Modoki, especially during northern winter and spring, and vice versa during northern fall, and similarly affect during northern summer. Furthermore, the dynamic movements of rainfall anomaly that are caused by El Niño Modoki and the conventional El Niño events spanned from the southwest during June-July-August (JJA) to throughout the northeast ending in March-April-May (MAM).

A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era

AbstractA great deal of expertise in satellite precipitation estimation has been developed during the Tropical Rainfall Measuring Mission (TRMM) era (1998–2015). The quantification of errors associated with satellite precipitation products (SPPs) is crucial for a correct use of these datasets in hydrological applications, climate studies, and water resources management. This study presents a review of previous work that focused on validating SPPs for liquid precipitation during the TRMM era through comparisons with surface observations, both in terms of mean errors and detection capabilities across different regions of the world. Several SPPs have been considered: TMPA 3B42 (research and real-time products), CPC morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP; both the near-real-time and the Motion Vector Kalman filter products), PERSIANN, and PERSIANN–Cloud Classification System (PERSIANN-CCS). Topography, seasonality, and climatology were shown to play a role in the SPP’s performance, especially in terms of detection probability and bias. Regions with complex terrain exhibited poor rain detection and magnitude-dependent mean errors; low probability of detection was reported in semiarid areas. Winter seasons, usually associated with lighter rain events, snow, and mixed-phase precipitation, showed larger biases.

Characteristics of Precipitation Features and Annual Rainfall during the TRMM Era in the Central Andes

Abstract The central Andes extends from 7° to 21°S, with its eastern boundary defined by elevation (1000 m and greater) and its western boundary by the coastline. The authors used a combination of surface observations, reanalysis, and the University of Utah Tropical Rainfall Measuring Mission (TRMM) precipitation features (PF) database to understand the characteristics of convective systems and associated rainfall in the central Andes during the TRMM era, 1998–2012. Compared to other dry (West Africa), mountainous (Himalayas), and dynamically linked (Amazon) regions in the tropics, the central Andes PF population was distinct from these other regions, with small and weak PFs dominating its cumulative distribution functions and annual rainfall totals. No more than 10% of PFs in the central Andes met any of the thresholds used to identify and define deep convection (minimum IR cloud-top temperatures, minimum 85-GHz brightness temperature, maximum height of the 40-dBZ echo). For most of the PFs, available moisture was limited (<35 mm) and instability low (<500 J kg−1). The central Andes represents a largely stable, dry to arid environment, limiting system development and organization. Hence, primarily short-duration events (<60 min) characterized by shallow convection and light to light–moderate rainfall rates (0.5–4.0 mm h−1) were found.

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Abstract. Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed.

Evaluation of Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model

Abstract A new version of a real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall has been developed and implemented using a physically based hydrologic model. The purpose of this paper is to evaluate the performance of this new version of the GFMS in terms of flood event detection against flood event archives to establish a baseline of performance and directions for improvement. This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998–2010) using a global retrospective simulation (3-hourly and ⅛° spatial resolution) with the TMPA 3B42V6 rainfall. Four methods were explored to define flood thresholds from the model results, including three percentile-based statistical methods and a Log Pearson type-III flood frequency curve method. The evaluation showed the GFMS detection performance improves [increasing probability of detection (POD)] with longer flood durations and larger affected areas. The impact of dams was detected in the validation statistics, with the presence of dams tending to result in more false alarms and greater false-alarm duration. The GFMS validation statistics for flood durations >3 days and for areas without dams vary across the four methods, but center around a POD of ~0.70 and a false-alarm rate (FAR) of ~0.65. The generally positive results indicate the value of this approach for monitoring and researching floods on a global scale, but also indicate limitations and directions for improvement of such approaches. These directions include improving the rainfall estimates, utilizing higher resolution in the runoff-routing model, taking into account the presence of dams, and improving the method for flood identification.