Evaluation of NASA’s MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

Abstract

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S. Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

“…TRMM 3B42 has a lower probability of detection and lower false‐alarm rates than other products in both warm and cold seasons. MERRA reproduces the continental‐scale patterns of change observed in the CPC U.S. Unified gridded data in a reasonable manner, although it underestimates the magnitude of extremes, especially over the Gulf Coast regions (Ashouri et al, ), the value of the 99th percentile of precipitation was lower for MERRA than for CPC (Ashouri et al, ).…”

Input matters matter: Bioclimatic consistency to map more reliable species distribution models

“…Satellite climatic layers have some pitfalls as well, showing a general bias towards warmer and drier estimations that has been observed particularly in low and mid‐latitude regions. The issues at high latitudes are related to seasonal dynamics of inconsistent soil moisture, compared to in situ observations (Ashouri, Sorooshian, Hsu, Bosilovich, & Wehner, ; Bosilovich, ; Waltari et al., ; Yi et al., ). Vega et al.…”

“…() even found differences in up to c .10°C in mean temperatures of the more extreme months when comparing two different databases. Given all these drawbacks, the selection of appropriate bioclimatic data becomes a complex task in distribution modelling that should be carefully accomplished, considering its limitations, and not simply assuming that the use of a single database is optimal for all regions and scales (Ashouri et al., ; Blacutt et al., ; Yi et al., ).…”

MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling

“…We chose MERRA to produce global bioclimatic variables as, in several evaluations englobing several decades, it showed high reliability for water 34 and energy variables 35 at different scales and in different regions. To reinforce these validations, we have carried on a quantitative comparison between MERRA and Antarctic ground stations.…”