Scoping a field experiment: error diagnostics of TRMM precipitation radar estimates in complex terrain as a basis for IPHEx2014

Abstract

Abstract.A diagnostic analysis of the space-time structure of error in quantitative precipitation estimates (QPEs) from the precipitation radar (PR) on the Tropical Rainfall Measurement Mission (TRMM) satellite is presented here in preparation for the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014. IPHEx is the first NASA ground-validation field campaign after the launch of the Global Precipitation Measurement (GPM) satellite. In anticipation of GPM, a science-grade high-density raingauge network was deployed at mid to high elevations in the southern Appalachian Mountains, USA, since 2007. This network allows for direct comparison between ground-based measurements from raingauges and satellite-based QPE (specifically, PR 2A25 Version 7 using 5 years of data [2008][2009][2010][2011][2012][2013]. Case studies were conducted to characterize the vertical profiles of reflectivity and rain rate retrievals associated with large discrepancies with respect to ground measurements. The spatial and temporal distribution of detection errors (false alarm, FA; missed detection, MD) and magnitude errors (underestimation, UND; overestimation, OVR) for stratiform and convective precipitation are examined in detail toward elucidating the physical basis of retrieval error.The diagnostic error analysis reveals that detection errors are linked to persistent stratiform light rainfall in the southern Appalachians, which explains the high occurrence of FAs throughout the year, as well as the diurnal MD maximum at midday in the cold season (fall and winter) and especially in the inner region. Although UND dominates the error budget, underestimation of heavy rainfall conditions accounts for less than 20 % of the total, consistent with regional hydrometeorology. The 2A25 V7 product underestimates lowlevel orographic enhancement of rainfall associated with fog, cap clouds and cloud to cloud feeder-seeder interactions over ridges, and overestimates light rainfall in the valleys by large amounts, though this behavior is strongly conditioned by the coarse spatial resolution (5 km) of the topography mask used to remove ground-clutter effects. Precipitation associated with small-scale systems (< 25 km 2 ) and isolated deep convection tends to be underestimated, which we attribute to non-uniform beam-filling effects due to spatial averaging of reflectivity at the PR resolution. Mixed precipitation events (i.e., cold fronts and snow showers) fall into OVR or FA categories, but these are also the types of events for which observations from standard ground-based raingauge networks are more likely subject to measurement uncertainty, that is raingauge underestimation errors due to undercatch and precipitation phase.Overall, the space-time structure of the errors shows strong links among precipitation, envelope orography, landform (ridge-valley contrasts), and a local hydrometeorological regime that is strongly modulated by the diurnal cycle, pointing to three major error causes that are inter-related: (1) representation of concurren...

Evaluation of diurnal variation of GPM IMERG‐derived summer precipitation over the contiguous US using MRMS data

“…Warm orographic rains are thus likely to be underestimated by satellite observation (also Shige et al ). Duan et al () also investigated the precipitation radar QPE by the TRMM in the southern Appalachian Mountains and found that precipitation of small‐scale systems and isolated deep convection tends to be underestimated due to spatial averaging at the radar resolution. These explanations could also be applied to the underestimated precipitation maxima over the complex topography in the western US.…”

Comparative Ground Validation of IMERG and TMPA at Variable Spatiotemporal Scales in the Tropical Andes

“…, n tot is the number of time steps (total of N tot time steps), p is the precipitation intensity at a particular grid cell, and r is the aggregated spatial resolution (0.18 or 0.258). Rainfall detection is assessed by three categorical error scores: the accuracy index (ACC; Duan et al 2015), the probability of detection (POD), and the false alarm ratio (FAR):…”

Interannual Variation of Summer Rainfall in the Taipei Basin

“…For convenience, rainfall amounts for these three datasets are denoted as P (station), P (TRMM), and P (GBT), respectively. As noted by Duan et al (2015), the TRMM PR exhibits terrain bias. Thus, the TRMM PR and GBT data are calibrated with the area-mean station rainfall over three ranges of altitude: 0-100, 100-500, and 500-1000 m in areas 1-3, respectively, shown in the left-hand column of Fig.…”

“…Using only station data over the stationdense area to estimate the area-mean rainfall enables us to use this area-mean rainfall to calibrate TRMM rainfall and GBT rainfall proxy. Duan et al (2015) noted that calibration of rainfall proxy with satellite observations could be affected by orography. Therefore, the mountain height within the basin is divided into three ranges: 0-100, 100-500, and 500-1000 m. To save space, a detailed calibration procedure for both TRMM rainfall and GBT rainfall proxy data over the entire analysis domain (encircled by the red line in Fig.…”