Users’ Guide to
Imagery with Heavy Rainfall Potential Areas

July 2015 (Ver. 4)
Japan Meteorological Agency

Document control
DateIssue

Status and changes

March 2011
March 2012

Ver. 1A
Ver. 2

Original Issue
Title
1. Introduction
3. Product specifications(1) Coverage(3) Data format

5. Comparison with rainfall
August 2014

Ver. 3

3. Product specifications

July 2015

Ver. 4

Change of satellite data
source from MTSAT toHimawari-8
Addition of comparison
between MTSAT andHimawari-8 data

(1) Coverage(4) Color assignment (Fig. 3)

Specification changes
DateChanges

April 2011
March 2012

Image data format
Threshold values for South Pacific Islands
Coverage for Southeast Asia

August 2014

Coverage for Southeast Asia

July 2015

Switch of satellite data source from MTSAT to Himawari-8

1. Introduction
This users’ guide gives a summary of Imagery with Heavy Rainfall Potential Areas –a product derived from Himawari-series satellites (MTSAT and Himawari-8) for theWMO CBS Severe Weather Forecasting Demonstration Project (SWFDP). Chapter 2
outlines the product itself, Chapter 3 describes its specifications, Chapter 4 briefly
summarizes the basic algorithm used, Chapters 5 and 6 report on the results of relatedcomparison, and Chapter 7 highlights a number of points to note regarding the product.
2. Outline
Imagery with Heavy Rainfall Potential Areas is a satellite product in imagery form
providing information about the possibility of rainfall associated with deep convective
clouds. Areas where rainfall is possible or probable (referred to as “rainfall potentialareas”) are identified by detecting convective clouds that have tops with a low
brightness temperature because the tops of such clouds causing heavy rainfall reachhigher altitudes or even the tropopause.
This product helps to clarify the possibility of rainfall, but does not provide actual
measurements of rainfall or estimations of precipitable water amounts because theimagers on board the Himawari-series satellites used capture infrared radiance fromcloud tops but extract few signals relating to internal cloud structures.

3.

Product specifications(1) Coverage
The product covers two areas, one stretching from 30°N to 15°S and from 90°E to165°E for SWFDP in Southeast Asia (Fig. 1). The other extends from 10°N to 35°S
and from 140°E to 145°W for the Severe Weather Forecasting and Disaster RiskReduction Demonstration Project (SWFDDP) – a regional subproject of SWFDP in
the South Pacific Islands (Fig. 2).

(30°N, 90°E)(15°S, 165°E)

Figure 1 Product coverage for Southeast Asia

(10°N, 140°E)(35°S, 145°W)
Figure 2 Product coverage for the South Pacific Islands(2) Spatial resolution
The spatial resolution of each pixel is 0.05° in both latitude and longitude.
(3) Data format
Image data comply with the specifications of the Joint Photographic Experts Group(JPEG) format.

(4) Color assignment
Rainfall potential areas are colored, as shown in Figs. 3 and 4. Cloud imagery isbased on satellite observations on the long-wave infrared band (10.4 μm), andgrayscale shading is used to show brightness temperature. Lighter colors represent
lower temperatures, and darker colors show higher temperatures. Convective clouds
that may produce rainfall are shown in magenta.

Figure 3 Product sample for Southeast Asia

Figure 4 Product sample for the South Pacific Islands4. Basic algorithm
The brightness temperatures of the three Himawari-8 infrared bands (6.2 μm (T6.2),
10.4 μm (T10.4) and 12.4 μm (T12.4)) are used for detection of deep convective clouds.
The concepts below are adopted to detect convective clouds with high tops.
-T6.2 is lower than T10.4 and T12.4 when clouds are in the development stage andtheir tops do not reach the tropopause.
T6.2, T10.4 and T12.4 are almost identical when convective cloud tops reach thetropopause.
T6.2 may be higher than T10.4 when cloud tops overshoot into the stratosphere.
The temperature difference between T10.4 and T12.4 increases when thin cirrus
clouds are observed.

Threshold values for the detection of deep convective clouds are adjusted to placepriority on the improvement of detection probability.
5. Comparison with rainfall
The probability of detection (POD) and the success ratio (SR), which are often usedin forecast verification, are calculated to evaluate the product. Rainfall potential areas
based on data from the MTSAT imager are compared on a pixel-by-pixel basis to rainfall
retrieval data from microwave imagers on board the polar orbiting satellites in the Global
Satellite Mapping of Precipitation (GSMaP*) (hereafter simply referred to as “rainfall”). A
rain rate of 20 mm/h is adopted as the criterion for heavy rainfall. The results ofcomparison from January through October 2011 are shown in Table 1.

Table 1 Results of comparison for rainfall of 20 mm/h or more

POD
SR

Southeast Asia
0.812
0.012

Area
South Pacific Islands
0.805
0.012

POD = H / (H + M), known as the hit rate (HR), shows the ratio of correctly identified rainfall.
SR = H / (H + F) gives the ratio of the rainfall potential area where rainfall is actually observed.
Situations are categorized in three ways based on cloud detection and rainfall presence:-“H” denotes hits (i.e., rainfall is observed in the rainfall potential area).
“M” denotes missed detections (i.e., rainfall is observed outside the rainfall potential area).
“F” denotes false alarms (i.e., rainfall is not observed in the rainfall potential area).

Potential areas

Yes
NoRainfall (≥ 20 mm/h)
Yes
NoH
F
M

* The GSMaP Project is sponsored by JST-CREST and promoted by the JAXA Precipitation Measuring
Mission (PMM) Science Team. GSMaP products are distributed by the Earth Observation ResearchCenter of the Japan Aerospace Exploration Agency.

Rainfall potential areas and visualized images of GSMaP rainfall retrieval data areshown in Figs. 5 and 6.
Figure 5 indicates a close relationship between rainfall potential areas and heavy
rainfall areas. The potential areas across Thailand, Lao PDR and Vietnam (left) show
good correspondence to heavy rainfall areas with a rain rate equal to or more than 20
mm/h (right). As shown by the POD score, the potential areas match around 81% of theactual heavy rainfall areas.

Figure 5 Comparison of rainfall potential areas and GSMaP rainfall areas(1632 UTC, 30 September, 2011)

Figure 6 shows overestimation of heavy rainfall areas. Rainfall potential areas overMyanmar, Thailand, Lao PDR and Vietnam correspond to actual rainfall areas, althoughmost of these have a rain rate of less than 20 mm/h. As shown by the success ratio (SR)in Table 1, around 1.2% of potential areas experienced heavy rainfall equal to or morethan 20 mm/h, meaning that almost 99% of such areas experienced rainfall of less than20 mm/h or no rain.
For reference, the SR for rainfall with a rain rate of more than 0.1 mm/h is around80%.

Figure 6 Comparison of rainfall potential areas and GSMaP rainfall areas(1532 UTC, 16 August, 2011)
6. Comparison between MTSAT and Himawari-8
Comparison of rainfall potential areas based on Himawari-8 data and MTSAT datashows very close correspondence (Figs. 7 and 8) because the bands used are similar
for both satellites (see Table 2).
Figure 7 shows rainfall potential areas for Southeast Asia derived from MTSAT data(left) and Himawari data (right) every six hours, and Figure 8 shows the same for theSouth Pacific Islands. Distribution in these areas is similar for all times.
Table 2 Observation wavelengths [μm] used for detection of deep convective clouds
MTSAT
Himawari-8
6.7
6.2 (band 8)
10.8
10.4 (band 13)
12.0
12.4 (band 15)

0000 UTC

0000 UTC

0600 UTC

0600 UTC

01200 UTC

01200 UTC

01800 UTC

01800 UTC

0000 UTC

0000 UTC

Figure 7 Left: imagery derived using MTSAT data from 0000 UTC on 15 June 2015 to0000 UTC on 16 June 2015 every six hours. Right: imagery derived from Himawari-8data.

0000 UTC

0000 UTC

0600 UTC

0600 UTC

01200 UTC

01200 UTC

01800 UTC

01800 UTC

0000 UTC

0000 UTC

Figure 8 Left: imagery derived using MTSAT data from 0000 UTC on 15 June 2015 to0000 UTC on 16 June 2015 every six hours. Right: imagery derived from Himawari-8data.

7. Points to note-Rainfall potential areas indicated by this product do not always correspond toareas of actual rainfall.
Dense cirrus clouds may be misclassified as deep convective clouds.
Clouds smaller than the spatial resolution of satellite observation may remain
undetected.
The specifications of this product are subject to change without notice.
The product is derived using Himawari-8 data from July 2015.