The Latitudinal Variability of Oceanic Rainfall Properties and Its Implication for Satellite Retrievals: 1. Drop Size Distribution Properties

Abstract

In this study, we analyze an in situ shipboard global ocean drop size distribution (DSD) 8‐year database to understand the underpinning microphysical reasons for discrepancies between satellite oceanic rainfall products at high latitudes reported in the literature. The natural, latitudinal, and convective‐stratiform variability of the DSD is found to be large, with a substantially lower drop concentration with diameter smaller than 3 mm in the Southern hemisphere high latitude (S‐highlat, south of 45°S) and Northern Hemisphere polar latitude (N‐polar, north of 67.5°S) bands, which is where satellite rainfall products most disagree. In contrast, the latitudinal variability of the normalized oceanic DSD is small, implying that the functional form of the normalized DSD can be assumed constant and accurately parameterized using proposed fits. The S‐highlat and N‐polar latitude bands stand out as regions with oceanic rainfall properties different from other latitudes, highlighting fundamental differences in rainfall processes at different latitudes and associated specific challenges for satellite rainfall retrieval techniques. The most salient differences in DSD properties between these two regions and the other latitude bands are: (1) a systematically higher (lower) frequency of occurrence of rainfall rates below (above) 1 mm h‐1, (2) much lower drop concentrations, (3) very different values of the DSD shape parameter (μ0) from what is currently assumed in satellite radar rainfall algorithms, and (4) very different DSD properties in both the convective and stratiform rainfall regimes. Overall, this study provides insights into how DSD assumptions in satellite radar rainfall retrieval techniques could be refined.

Spaceborne Cloud and Precipitation Radars: Status, Challenges, and Ways Forward

“…Such in situ measurements could also contribute in refining a‐priori assumptions (e.g., how ice D m and IWC at a given temperature are related to reflectivities and how they covary), which, as recently highlighted by Protat et al. (2019a) for rain, may be regime/latitude dependent. In situ measurements below the freezing level remain essential to validate full‐column retrievals. Scattering models are still sources of large uncertainties especially at frequencies above the Ka band.…”

Measurements of Rainfall Rate, Drop Size Distribution, and Variability at Middle and Higher Latitudes: Application to the Combined DPR-GMI Algorithm

“…The total number of 1-s DSDs was 4142, all quality controlled (J. Jensen, NCAR, personal communication). (c) A very large (arguably the largest ever quality controlled) set of DSDs acquired over the open ocean (OceanRain) described by Klepp et al [33] and Protat et al [22,23]. (d) Simulations of gamma DSDs with uncorrelated N W , D m , and shape parameter (µ).…”

“…The functional form of h(x) does not necessarily have to be specified as long as its moments are finite. A flexible form used by Thurai and Bringi [9], Protat et al [22], and Duncan et al [24] is the generalized gamma, which has two shape parameters. The N 0 is termed the 'normalized' intercept parameter and D m , defined as M 4 /M 3 , is termed the mass-weighted mean diameter.…”

“…In the current version (V6) of the CMB, the D m is not included in the state vector; rather, it is tied to the Ku-band radar reflectivity (Z e ) profile and a generalized Hitschfeld-Bordan (H-B; [19]) attenuation correction methodology [15]. Whereas the N W parameter is set a priori at five different altitudes with assumed spatial correlation based on numerical modeling [15] and other ground-validation (GV) radar estimation of the same [20], the mean a priori values of N W in rain of 15,660 mm −1 m −3 for convective and 7420 for stratiform rain [21], which largely reflects the TRMM legacy and could be significantly biased in the mid-upper latitude N and S hemispheres as shown by Protat et al [22,23] and Duncan et al [24]. Given the large variability of N W and uncertainty of accuracy in light rainfall, a new CMB formulation was recently implemented [25] to better account for the inverse relation between N W and D m , which has been verified in numerous GPM-GV field programs [26,27].…”

“…In view of these recent CMB algorithmic developments, our main goal in this study is to compare the statistical properties of DSD parameters and statistical relationships between radar observables and DSD parameters using our measured DSDs with assumptions currently held in the CMB algorithm. The focus of our study is on characterizing such properties in order to complement recent studies where such properties and relationships have been derived for a variety of other different rain types [22,23,27,29].…”