Exploring light rain in the trades as observed by satellite- and ground-based remote sensing instruments
Jörg Burdanowitz1,2, Louise Nuijens1, Christian Klepp3, Bjorn Stevens1, Stephan Bakan1
(1) Max Planck Institute for Meteorology, Hamburg, Germany (2) International Max Planck Research School on Earth System Modeling (IMPRS-ESM), Hamburg, Germany (3) KlimaCampus, Hamburg, Germany
Introduction
Over the subtropical oceans a trade inversion lets only shallow clouds
develop that are regulating the atmospheric heat and moisture budget.
This frequently leads to shallow (warm) rain showers. Due to their small
dimensions and usually weak intensities they still hold large uncertainties
in modeling and observing the hydrological cycle within the Subtropics.
Conclusions
1. Do satellite climatologies resolve light rain from shallow clouds?
2. Does the character of light rain matter for its detection by the satellite?
 Satellite climatologies are partly able to resolve oceanic light rain: GPCP misses most events, HOAPS and TMPA
are similarly reliable. (Among all, HOAPS suffers most from lack of SSM/I overpasses, TMPA has higher spatiotemporal resolution)
 Rain showers need to reach a minimum size and echo fraction (about 3% at least) in order to be detected by
satellite instruments.
Photo by L. Nuijens.
Motivation
1) Is light rain detected by satellites?
→ Pixel rain rates of data sets with different spatial resolutions are not directly comparable!
Averaged onto the same area: S-Pol shows frequently occurring light rain events
except for three days of RICO, GPCP misses most light rain events, HOAPS and
TMPA miss only some of them (Fig. 4). HOAPS misses rain when number of
SSM/I overpasses is low.
→ Lacking SSM/I overpasses are main reason to miss rain
Cumulative daily rain rate distribution confirms that GPCP misses the light rain
rates predominantly (Fig. 6), HOAPS and TMPA show far more low rain rates.
→ GPCP is not suitable to resolve light rain over ocean
The area mean rain rate scales well with fraction of rain covered area (Fig. 5).
Figure 1: Light rain (<1mm/h) fraction of HOAPS 3.2 (1988-2005), field campaigns and mean trade-wind trajectory (red-dashed line).
→ Rain covered area matters more for area-mean rain than intensity
Figure 2: Rain rate frequency distribution of MRR pixel rain rates measured on Barbados Cloud Observatory (BCO) during Apr, 2010
to Apr, 2012 and S-Pol pixel rain rates measured from Nov, 2004 to Jan, 2005 (RICO).
Figure 3: Average rain rate (left) and light rain (<1 mm/h) fraction (right) along trade trajectory (1998-2005). Red colors indicate
wet (Jun-Nov), blue dry (Dec-May) season. Bars show the spread between satellite products HOAPS, TMPA and GPCP.
Fig. 1
GPCP
HOAPS
100
TMPA
75
50
25
10
0
Figure 5: Overview of how pixel rain rates contribute to area rain rates for RICO NE-radar domain
(NDJ 1998-2005). Colors represent the relative number of pixels with rain (blue to red).
only NE radar domain:
150 km
(BCO)
(RICO)
(Z=148*R1.55)
S-Pol radar on
Barbuda Island
low SSM/I
Fig. 3a
Fig. 3b
Methods and data
Data acronyms
Hamburg Ocean Atmosphere Parameters and fluxes
from Satellite data v.3.2 (HOAPS-C and -S)
1) RICO: Is light rain detected by satellites?
- all products averaged to same area (NE-radar
domain) of RICO campaign (Rauber et al., 2007)
- ground-based radar as reference to satellite data
- main focus on detectability instead of rain amounts
- S A T E L L I T E
2) RICO: What limits the detectability of rain?
- directly compare HOAPS-S swath data to S-Pol
radar images in order to understand under which
conditions rain echos can be detected
- unveil differences due to spatiotemporal disparity
D A T A S E T S -
- R A D A R
TMPA
(gridded)
HOAPS-C
(gridded)
HOAPS-S
(swath, ungridded)
S-Pol Radar
(RICO, polar grid)
Micro Rain Radar
(BCO, point-wise)
Spatial resolution
1°
0.25°
0.5°
~ 0.3°
100 m
-
Time resolution
daily
6(3)-hourly
6-hourly
infrequently
~ 20 min
2 min
Period used
1998-2005 1998-2005
1988 (1998)2005
Nov, 2004- Jan,
2005 (RICO)
Nov, 2004- Jan,
2005 (RICO)
Apr, 2010-Apr,
2012
Data sources /
add. information
SSM/I, IR,
gauges
SSM/I
RICO: Z=89*R1.52
TRMM:Z=148*R1.55
24 GHz MRR
SSM/I, TMI, IR, SSM/I
PR, gauges
Average rain echo size and echo fraction play major role for detectability (Fig. 7):
HOAPS-S detects small showers only when echo fraction is not too small (> 3%)
→ Isolated, small showers have lowest probability to be detected
Satellite data: HOAPS-S
3
0,8
0,6
0,4
R² = 0,9483
0,2
dBZ
Figure 7: Individual cases of HOAPS-S swath data (rain rate [mm/h]) without land flag and S-Pol
radar data (Reflectivity [dBZ]) for specific RICO cases. Note: Guadeloupe was excl. from
analysis because of constantly showing rain (false alarm).
Contact:
[email protected]
2
1
0
0
References:
Andersson, A., C. Klepp, K. Fennig, S., H. Grassl, J.Schulz, 2011: Evaluation of HOAPS-3 Ocean Surface Freshwater Flux Components.
J. Appl. Meteor. Climatol., 50, 379–398. doi: http://dx.doi.org/10.1175/2010JAMC2341.1
Rauber, R. M., et al., 2007: Rain in shallow cumulus over the ocean: The RICO campaign. Bulletin of the American Meteorological
Society, 88 (12), 1912–1928.
Sandu, I., B. Stevens and R. Pincus, 2010: On the transitions in marine boundary layer cloudiness, Atmos.Chem. Phys.,10, 2377-2391.
HOAPS-S shows far higher echo fractions than S-Pol radar (Fig. 8a), the distribution aligns to a polynomial due to the difference in spatial resolution. As a
consequence, the echo rain rates are only poorly correlated (Fig. 8b).
Radar: S-Pol
D A T A S E T S -
GPCP 1DD
(gridded)
TRMM Multi-Precipitation Analysis v.6 (TMPA)
General Precipitation Climatology Project (GPCP 1DD)
2) What limits the detectability of rain?
S-Pol echo fraction [-]
‘Shallow rain’ or ‘light rain’?
In the trades, clouds are usually kept shallow by a trade
inversion. These clouds in most parts rain with light
intensities but are not limited to those. That means
‘shallow rain’ is not necessarily ‘light rain’!
As satellite climatologies do not contain information of
cloud thickness, the study is limited to an area of light
rain mostly originating from shallow clouds which means
‘light rain’ is usually also ‘shallow rain’.
Figure 6: Cumulative distribution of daily averaged rain rates of satellite climatologies and S-Pol
radar, averaged over NE radar domain for the RICO period (Nov 28, 2004 to Jan 24,
2005). The blue line corresponds to an up-scaled version of TMPA (0.25° → 0.5°).
S-Pol echo rain rate [mm/h]
Fig. 2
Figure 4: Rain rates of satellite data and S-Pol radar, averaged over NE radar domain for the RICO
period (Nov 28, 2004 to Jan 24, 2005). The radar reflectivity (dBZ) was converted to rain
rate (mm/h) using the TRMM Z-R relation for shallow convection. Grey shades represent
periods of only 1-2 SSM/I satellite overpasses per day.
0
0,2
0,4
0,6
HOAPS-S echo fraction [-]
0,8
0 0.3
1
2
3
HOAPS-S echo rain rate [mm/h]
Figure 8: Scatter plots of echo fraction (left: 8a) and echo rain rate (right: 8b) of HOAPS-S swath
data (w/o land flag) and S-Pol radar data for all raining RICO cases (120). HOAPS
threshold excludes rain rates smaller than 0.3 mm/h.
Rain fraction [%]
Along the mean trade-wind trajectory (Sandu et al., 2010) light rain occurs predominantly (Fig. 1), shown from
HOAPS satellite climatology (Andersson et al., 2011). Therefore this region (“trades”) has been chosen to
compare mean rain rates of three different satellite climatologies over the ocean (Fig. 3a). Besides seasonal
differences illustrated by the ongoing Barbados Cloud Observatory (BCO, Fig. 2), the three satellite products
HOAPS, TMPA and GPCP agree well on the main rain patterns. In contrast, the spread in the light rain fraction
(Fig. 3b) is much larger, chosen a constant threshold of 1mm/h (as in Fig. 1). The reason for that discrepancy is
the different spatial resolution as rain rates are strongly related to their pixel size.