advances in the assessment of the mw-ir blended technique for rain rate measurement by remote

DUSSELDORF 27-28 May, 2003 "MUSIC/CARPE DIEM Workshop

Advances in the assessment of the MW-IR blended technique for rain rate measurement by remote sensing

Vincenzo Levizzani and Francesca Torricella

Satellite Meteorology GroupISAC-CNR Italy


Previous applications devoted to the production of rainfall accumulations on a 6 to 48 hours basis or more

The application to flood forecasting requires to assess the reliability of the method in producing instantaneous rain rate maps

To this end we work along a few main directions:

• Improving (especially over land) the algorithm applied to MW TB;

• Assessing the capability of the statistical histogram matching approach in propagating the information on rainfall between two MW calibrations; • Validation of rain rate fields produced by means of Turk’s technique comparing them with raingauges measurements (from dense, nearly homogeneous, short time reporting networks) from literature.


Available at SatMet group in Bologna the new GPROF version 6 (Goddard profiling algorithm, Kummerow et al., 2001)

After careful comparison of GPROF5 RR estimates with ground validation data, authors are releasing the GPROFv6, modified to match PR and ground observations.

We needed to:• adapt the GPROF structure in order to manage SSM/I data, needed for

proper coverage of non-tropical areas, i.e. to prepare an interface to adapt

SSM/I data to the RU software; • insert the new algorithm in the RU structure, and compare its performances with the NESDIS (Ferraro et al., 1997) algorithm.• test the new algo on weather conditions, geographical areas and cases relevant to the Projects.

Improving over land the algorithm applied to MW TB


The GFROFv6: an algorithm for TRMM/TMI , SSM/I and AMSR-E instruments (McCollum and Ferraro, 2003)

The new version eliminates the global high bias with respect to gauges

This is done using coincident TMI and PR to derive relationships betweenRR and TB85V for both stratiform and convective rain

The probability of convection is derived form PR-estimates and used tocalibrate PMW* predictors of convection

The resulting rainfall estimates compare quite well with PR data and GPCP**monthly rainfall product.

* Passive MW** GPCP: Global Precipitation Climatology Project


LATITUDE

GPROFv6 performancesmean monthly rain (1998-2000)

mm

Zonal rainfall profilesusing the three rainfallproducts (v5, v6, PR).

Overall v6 is relativelyunbiased respect to GPCP.

underestimation

overestimation


Open issues in GPROFv6(applied to SSM/I data)

• No rain detected at high latitudes (where there is less convective rainfall)

• No rain estimation over frozen surfaces such as snow

MONTHLYMEANJAN 2002


Assessing the capability of the histogram matching Assessing the capability of the histogram matching approach in propagating the information on approach in propagating the information on

rainfall between two MW calibrationsrainfall between two MW calibrations

The method follows to some extentthe evolution of the event before-after the MW calibrationPrevious calibration: 3 hours old!

10 Nov 2001 08:00 UTC

MW cal

08:30 UTC

08:03 UTC

3 25mm/h


From the previous slide it is apparent…

The RR values are depressed due to the smoothing process involved in the method (i.e. the TB-RR relationships derived from coincident data are averaged on 5 x 5 boxes regions to avoid discontinuities at box boundaries). For non-global applications avoid the smoothing (?)

lat0

lon0 +lon

overpass-2 swath (TMI)

overpass-1swath(SSM/I)

lon0

grid

Global grid for geo-locatingRR/TB relationships


Validation of rain rate fields produced Validation of rain rate fields produced by means of Turk’s techniqueby means of Turk’s technique

Validation of an Operational Global Precipitation Analysis at Short Time Scales F. Joseph Turk, Elizabeth E. Ebert, Hyun-Jong Oh, Byung-Ju Sohn, Vincenzo Levizzani, Eric A. Smith, and Ralph Ferraro

Proc. 1st IPWG Workshop, Madrid, 23-27 Sept., 2002, in press.

Rain gauges-RU scatter plotsfor several time average intervals(1° spatial scale)

Gauges from the AWS (Automated WeatherStation) network of the KMA (Korean Meteorological Agency)



Gauge averaging time: 2 minutes window centered about the GMS observation timeAs expected all three parameters improve as either the averaging period is increased or the grid size is coarsened!

0.50.5

2



Gauge averaging time: 20 minutes window centered about the GMS observation time. Sharp improvement when widening the time window.

0.5

0.5

2


Multispectral characterization of storm top

Vincenzo Levizzani, Elsa Cattani, Francesca Torricella and Maria João Costa

Satellite Meteorology GroupISAC-CNR Italy


Multispectral studies of cloud top to

identify severe weather features.

For example characterization of plumes

of ice crystals and link their formation to

precipitation formation mechanisms. The

idea is to explore the forecasting

potential

Observations of storm top plumes

suggest that very small ice crystals are

responsible for large increases in the

cloud top reflectance field at 3.7 μm.

Radiative transfer simulations using

several ice crystal habits indicate that

indeed small ice crystals are a likely

cause of such high reflectance values.

Only certain types of crystals, whose

dimensions are sufficiently small, are

compatible with reasonable plumes

optical depths and therefore correctly

reproduce the observed reflectance and

brightness temperature values.


8 Nov. 1998

Multispectral image from

TRMM VIRS over the

Kwajalein Atoll with

superimposed PR-derived

rain areas (stippled pixels).

The graphs report cloud top

temperature vs effective

radius.

While the 3.7 m product

(top) correctly identifies the

cloud top structure and

delimits the various

microphysical zones, the 1.6

m channel response

(bottom) suffers from

contamination from the lower

levels in the cloud. Numbers

on the graphs refer to the

numbered boxes in the

image.

Research conducted in

cooperation with D.

Rosenfeld, Hebrew Univ.

Jerusalem.

Multispectral studies of cloud top with applications to precip. estimations.


The classification scheme of convective clouds into microphysical zonesaccording to the shape of the temperature – effective radius relations

Note that in extremely continental clouds re at cloud base is very

small, the coalescence zone vanishes, mixed phase zone starts at T<-15oC, and the glaciation can occur at the most extreme situation at the height of homogeneous freezing temperature of –39oC. In contrast, maritime clouds start with large re at their base, crossing the

precipitation threshold of 14 m short distance above the base. The deep rainout zone is indicative of fully developed warm rain processes in the maritime clouds. The large droplets freeze at relatively high temperatures, resulting in a shallow mixed phase zone and a glaciation temperature reached near –10oC

0 5 10 15 20 25 30 35

-40

-30

-20

-10

0

10

20

reff

T [

oC

]

m]

Glaciated

Mixed PhaseRainout

Coalescence

General

Diffusional growth

0 5 10 15 20 25 30 35

-40

-30

-20

-10

0

10

20

reff

T [

oC

]

m]

Glaciated

Mixed Phase

Rainout

Coalescence

Maritime

0 5 10 15 20 25 30 35

-40

-30

-20

-10

0

10

20

reff

T [

oC

]

m]

Glaciated

Mixed Phase

Coalescence

Continental - moderate

Diffusional growth

0 5 10 15 20 25 30 35

-40

-30

-20

-10

0

10

20

reff

T [

oC

]

m]

Glaciated

Mixed Phase

Continental - extreme

Diffusional growth

Rosenfeld and Lensky, 1998

advances in the assessment of the mw-ir blended technique for rain rate measurement by remote

Documents

pr data

ssmi data

rainfall estimates

new algorithm

convective rainfall

coincident data

passive mw

rain estimation