WMO/CIMO/TECO-2016, Madrid, Spain, 27-29 September 2016 SESSION 4 – Challenges and opportunities for continuous improvement in observing technologies

Advanced weather radar application technology at JMA

Seiichiro Kigawa1, Toshihiro Hayashi1 and Takeshi Nishimura1

1 Japan Meteorological Agency (JMA), Tokyo, Japan

Abstract

The Japan Meteorological Agency (JMA) has operated a radar-based precipitation

analysis system since 1982. With this approach, radar precipitation observation data

are calibrated using the results of rain gauge observation by JMA and other central/local

government bodies. Both types of data are essential to the formulation of various

advanced products such as Nowcasting, Quantitative Precipitation Estimation (QPE)

and Quantitative Precipitation Forecasting (QPF), which are in turn critical for the

issuance of weather forecasts and warnings.

JMA’s provision of the High-Resolution Precipitation Nowcast (HRPN) product

(spatial/temporal resolution: 250 meters/5 minutes), which was launched in August

2014, requires the comprehensive use of various types of observations data collected in

real time. These include information from more than 9,000 rain gauges, 33 wind

profilers, 16 radiosondes, 20 JMA C-band weather radars, and X-band radars managed

by Japan’s Hydrological Services under the Ministry of Land, Infrastructure, Transport

and Tourism (MLIT). Prediction for the HRPN product involves the extrapolation of

rainfall distribution movement and trends with an orographic effect and dynamical

estimation, which supports analysis of three-dimensional atmospheric and cloud

physical parameters combined with data from various upper-air and surface-base

observation networks. The HRPN product is a crystallization of information from multiple

observing systems and cutting-edge estimation technologies, and is expected to

contribute to the goals of the WMO Integrated Global Observing System (WIGOS).

This report gives a summary of the HRPN product, outlines related challenges and

provides information on future work.

1. Weather radar application at JMA

The Japan Meteorological Agency (JMA) has operated a weather radar-based

precipitation analysis system since 1982. Today the system provides data for a variety

of nowcasting products that are used directly in JMA’s weather forecasting and

warning/advisory services (Figure 1), including nowcasts and Numerical Weather

Prediction (NWP). Nowcasts are used for real-time nowcasting products, Quantitative

Precipitation Estimation (QPE) and Quantitative Precipitation Forecasting (QPF).

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Hydrogeological indices for variables such as soil water and runoff are calculated using

QPE/QPF with a tank model. These products and hydrogeology indices are critical in

the issuance of weather forecasts and warnings

As Japan’s operational weather radars have conventionally taken a single-polarized

form, standard radar precipitation observation data are calibrated using the results of

rain gauge observation by JMA and other central/local government bodies.

Figure 1 Weather radar application at JMA

2. High-resolution Precipitation Nowcasts

2.1. Overview

JMA has issued Precipitation Nowcasts since summer 2004. On the 10th anniversary

of their introduction in August 2014, a new generation of High-resolution Precipitation

Nowcasts (HRPNs) became operational (JMA 2015). The specifications of HRPNs are

summarized in Table 1.

Spatial/temporal resolution

250 meters/5 minutes over land and coasts from 00 to 30 minutes ahead 1 km/5 minutes over land and coasts from 35 to 60 minutes ahead 1 km/5 minutes from coasts from 00 to 60 minutes ahead

Update frequency Every 5 minutes Initial data Horizontal precipitation distribution analyzed from radar,

rain gauge and upper-air observation information Vertical atmospheric profiles analyzed from radiosonde

and cumulonimbus cloud feature information Forecast process Kinetic: non-linear motion/intensity extrapolation with

orographic effect Dynamic: vertically one-dimensional convective model with

calculation relating to raindrop generation, precipitation

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and evaporation Convective initiation: three triggers: (1) downflow caused

by heavy rainfall; (2) temporal variations in surface temperature and water vapor; (3) intersection of arch-shaped thin echoes

Elements of distributed data

Precipitation intensity 5-minute cumulative precipitation Prediction uncertainty Analysis error sources

Table 1 Specifications of High-resolution Precipitation Nowcasts

2.1.1. Spatial/temporal resolution and update frequency

The spatial grid point interval of the new nowcasts has been shortened from the

conventional 1 km to 250 m in order to enhance observation and forecasting capacity

for sudden and localized heavy rain. However, for the second half of the forecast period

and for open-sea areas, spatial resolution has been left at 1 km to limit the size of data

files distributed to users in consideration of analysis and prediction accuracy (Figure 2).

HRPNs are updated every five minutes.

Figure 2 HRPN spatial/temporal resolution and update frequency

2.1.2. Initial data

HRPNs require the comprehensive use of various types of observation data collected

in real time. These include information from more than 9,000 rain gauges operated by

JMA and other central/local government bodies, 33 wind profilers, 16 radiosondes, 20

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JMA C-band weather radars, and 39 X-band radars managed by Japan’s Hydrological

Services under the Ministry of Land, Infrastructure, Transport and Tourism (MLIT;

Figure 3). Initial precipitation intensity distribution is determined via three-dimensional

analysis of storms using radar echo intensity, Doppler velocity, rain gauge, surface and

upper-air observation data.

Data on vertical atmospheric profiles is used for prediction generation. The initial

values for such data are based on upper-air observations, and are updated from

comparison of cumulonimbus cloud profiles (echo top rising speed, ceiling height,

lightning count and rainfall amount) between radar/radio-based observation and

calculation using the Vertically One-dimensional Convective Model (VOCM). This model

is used to predict rainfall based on calculation of cloud particle/raindrop generation and

development, fallout and evaporation of raindrops in a climbing bubble, and is also used

to predict cumulonimbus cloud profiles.

Thus, HRPN multi-observing-system-based nowcasting products are superior to their

radar-based counterparts thanks to their focus on various observation data application

technologies.

Figure 3 Observation network data used in HRPNs

2.1.3. Forecast process

HRPN formulation involves two processes for rain distribution prediction in three

spatial dimensions: (1) high-resolution prediction based on extrapolation of the

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three-dimensional distribution of water content using VOCM for selected heavy rain

regions; and (2) low-resolution prediction based on a longer time interval and reduced

vertical calculation outside high-resolution prediction regions (Figure 4). The

data-processing functions are designed for prediction via a dynamical estimation

approach suitable for the forecasting of rapidly changeable rain phenomena

using a kinetic approach involving the extrapolation of phenomenon movement

trends.

Figure 4 3-dimensional prediction approach

Several heavy rain areas are selected for the high-resolution prediction illustrated on the left of

the figure. Other echoes are processed via low-resolution prediction. The results of these

predictions are combined for final surface rainfall prediction.

HRPNs enable the prediction of convection initiation for longer lead times against

rapidly developing heavy rainfall based on three phenomena associated with

cumulonimbus generation (Figure 5).

Downflow caused by heavy rainfall

Cell formation is predicted in locations where gust fronts converge the most strongly

with surrounding surface winds.

Temporal variations in surface temperature and water vapor

Cell formation is predicted in locations where surface winds converge in the boundary

between increases/decreases in surface temperature and water vapor.

Intersection of arch-shaped thin echoes

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Cell formation is predicted in locations where two thin echoes intersect based on the

assumption that such intersection causes temporary increases in updraft.

When one or multiple triggers are detected, rainfall is predicted using VOCM to simulate

the life cycle of convective cloud in high-resolution prediction.

Figure 5 Convection initiation

Among the three triggers for convective initiation, intersection of thin echoes and temporal

variation of surface temperature/water vapor require the convergence of surface wind.

2.1.4. Distribution data

HRPN products are provided to local weather offices, private weather companies and

the public to support close monitoring of heavy-rain areas and disaster prevention

activities.

HRPN data also contain information on prediction uncertainty based on estimation of

the magnitude of error included in the rainfall forecast. Knowledge of this uncertainty is

expected to be useful in applications such as determining high probability of downpours

and predicting the range of river water levels. Users are also informed of possible error

sources such as radar clutter, bright band, upper-air echo and hail in order to highlight

potential uncertainties in HRPN analysis.

2.2. Quick and easy access to HRPNs and related weather information

HRPNs are intended to support self-protection against sudden heavy rain. JMA’s

related web pages are designed to give users an overview of the situation with the

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minimum number of clicks to enable prompt evacuation for safety. These resources are

optimized for mobile-device and PC viewing (Figure 6).

Figure 6 JMA’s HRPN web resources

Mobile (left) and PC (right) resources are provided to support self-protection. Several options are

given for superimposition of information on areas where heavy rain, lightning and hazardous

winds are expected. Rainfall amounts from rain gauge observation can also be displayed.

Quick access

Users can zoom in/out of maps to find their location using landmarks such as roads,

railways and rivers. GPS information can also be used where available, and default

locations can be set to facilitate future usage.

User-friendliness

Options are provided for superimposition with heavy rain area movement and areas

at high risk of lightning and hazardous winds to facilitate understanding of local weather

conditions. Current areas of heavy rain and those expected within 30 minutes are

enclosed in yellow lines to highlight approaching downpours without the need for

time-consuming animation viewing. Shaded red rectangles can be overlaid to display

areas where lightning and tornadoes/other hazardous winds are likely based on

Thunder and Hazardous Wind Potential nowcasts. Landmark information can also be

superimposed. Users can personalize these options to support self-protective action.

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More than two million page views were recorded on 22 August 2014, just two weeks

after the resource was launched, when rain and thundershowers hit the whole of Japan

in association with the approach of Typhoon Vongfong. For more information, see

http://www.jma.go.jp/en/highresorad/.

3. Advantages of synthetic use of various observation networks

As described in Section 2.1.2, various types of observation (such as the use of two

distinct radar networks, surface-based and upper-air-based operation) are used

synthetically in HRPN generation. The advantages of combined processing of

information from various observation networks include:

More accurate atmospheric analysis

Provision of uncertainty/error information on analysis

These advantages support high-quality prediction as exemplified in HRPN generation.

The sections below specify how observation data are processed synthetically in

HRPNs.

3.1. Freezing height estimation

In radar composition processing for HRPN generation, the height at which a

temperature of 0°C is observed (referred to as freezing height) must be known to avoid

increased radar observation error caused by melting snowflakes. Freezing height can

be estimated from ring-shaped echo size based on radar data, vertical speed based on

wind profiler data, and temperature profiles based on surface and radiosonde

observation. A ring-shaped echo is interpreted as a radar’s bright band caused by

melting snowflakes around freezing height. The line of discontinuity in vertical speed

can be determined from observation data, and can be taken as the freezing zone

because lower speed above and higher speed below the line indicate the terminal

speeds of snowflakes and raindrops, respectively.

Figure 7 shows a freezing-height contours based on such observations. The chart

covers 7 JMA radars, 10 wind profilers and 2 radiosonde stations indicated by circles,

squares and triangles, respectively. Radar and wind profiler networks both provide

spatially sparse data with low uncertainty. Denser information derived from a

combination of surface and radiosonde observation data may have an increased bias

due to the uncertainty of vertical temperature profiles. The integrated system consisting

of radar, wind profiler, surface and radiosonde observation data provides more accurate,

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denser information on freezing heights than single observation-based estimation, as the

figure shows.

Figure 7 Freezing height estimation

Contour lines indicate metric 0°C temperature. Observation stations are marked with circles

(JMA radars), squares (wind profilers) and triangles (radiosondes). Small horizontal-scale curves

shown by contour lines are mainly derived from surface-based temperature observation.

3.2. Update on vertical atmospheric profiles

For HRPN prediction, vertical atmospheric profiles are estimated via the synthetic use

of various types of observation data from and around heavy rain areas. Data on vertical

atmospheric profiles are also used as input for HRPN prediction. Initial values for these

data are based on radiosonde observation results, and are updated via comparison of

cumulonimbus cloud profiles between observation and calculation. This is shown in

Figure 8, which illustrates (from left to right) the generation, development, maturation

and dissipation of a cumulonimbus cloud. The bottom part of the figure shows three

scenarios for vertical profiles based on environmental analysis, and the green-circled

elements in the four columns on the right indicate similarities to observation data. Here,

the middle candidate is selected as the best match for the observation data. The

selected vertical-profile data are used to modify operational vertical profiles in and

around the rain area. As this update processing is scheduled to take place every five

minutes, the updated vertical profiles can be used for prediction of the next initial time.

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This mechanism is supported by radar, surface (temperature, relative humidity, wind)

and upper-air (radiosonde, wind profiler) observation data, which provide more rational

vertical profiles than estimation based on the single observation system.

Figure 8 Update on vertical atmospheric profiles

In the top diagram, the horizontal axis indicates time and the vertical axis indicates altitude. It

shows cumulonimbus cloud profiles with observed echo top rising speed (represented by the

length of the upward arrow), echo top ceiling, echo top ceiling height (dashed line) and rainfall

amount (depth of the blue area).

The lower diagrams illustrate three possible scenarios calculated using the different vertical

profiles indicated in the graphs on the left. The latest and hypothetical vertical temperature

profiles are indicated by dashed blue and solid red lines, respectively.

In the first of the three scenarios, estimation produces a lower rising speed and ceiling height, no

lighting and little rainfall. In the third scenario, the rising speed and ceiling height are

overestimated but the rainfall amount estimate is reasonable. The second scenario is recognized

to provide the most reasonable estimation compared with observation data.

3.3. Radar composition and rainfall estimation

Radar composition in HRPN generation is designed to minimize the effects of clutter,

bright band and scan time differences based on estimation of radio propagation paths

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and horizontal wind speed data. Rainfall is estimated from radar composition with

correction for variables such as wind-induced drift and evaporation using data on wind,

temperature, humidity and rainfall as observed using rain gauges. These key features of

HRPN rainfall estimation are summarized in Figure 9.

(1) A radio propagation path traced from radar is first estimated using vertical

atmospheric profiles. (2) The observation height is allocated adaptively on the basis of

radio propagation path estimation and detection of clutter and bright band. (3) Echo

horizontal drift as observed during a volume scan is corrected. (4) Rain rate data

obtained from individual radar observations are composited using two methods to

create a national mosaic. (5) Drift and evaporation of raindrops during their fall are

corrected. (6) Radar observation data are adjusted in consideration of rain gauge

observation data. Before this process, rain gauge data uncertainty induced by wind is

corrected.

Figure 9 Characteristics of radar composition and rainfall estimation

The dashed blue line in the figure indicates observation height allocated on the basis of radio

propagation estimation and clutter/bright-band detection. The allocation is normally higher above

clutter or lower near the bright band.

This mechanism is also supported by data from radar, surface (temperature, relative

humidity, wind, rainfall amount) and upper-air (radiosonde, wind profiler) observations,

which provide more rational vertical profiles than estimation based on a single observing

system. The combination of these observations enables accurate rainfall estimation and

the provision of related uncertainty information.

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It is important to note that radar and rain gauge observation data are used

synthetically; radar data are not calibrated against rain gauge data as in conventional

precipitation analysis because one of the radar networks used for HRPNs is a

dual-polarized system, which enables more accurate rain rate estimation than a

single-polarized system. Wind also plays an important role in rainfall estimation to

connect remotely sensed (plane) radar data with directly measured (pinpoint) rain

gauge data (Figure 10). The current HRPN algorithm is not designed to account for

orographic effects or the influence of buildings on wind near rain gauges, and therefore

requires further improvement. In this context, estimation of wind fields where orographic

effects or buildings near the rain gauge may influence data is currently being

researched using computational fluid dynamics (CFD) simulation. Further, wind-induced

uncertainty in rain gauge data vary with precipitation intensity, precipitation type

(rain/snow) and drop size distribution (DSD) as well as wind speed. In future work, DSD

information estimated using disdrometer or dual-polarized radar data will be researched

to connect plane radar data with pinpoint rain gauge data by adjusting for effects on

various types of precipitation particles.

Figure 10 Wind-induced horizontal drift

The current HRPN operational algorithm is designed to correct wind-induced horizontal drift that

occurs when raindrops fall based on the wind speed of several layers (top). The nature of

complex raindrop tracks associated with air flow (bottom left) and the orographic concentration of

raindrops are currently being researched.

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In short, the HRPN product in operational service is a crystallization of information

from multiple observing systems and cutting-edge estimation technologies. HRPNs are

expected to contribute to the goals of the WMO Integrated Global Observing System

(WIGOS).

4. Current challenges and future work

HRPNs are not technically perfect, and current developmental work is focused on

accurate prediction of major downpours. One development task involves the

establishment of a sophisticated algorithm to connect plane radar data with pinpoint rain

gauge data in consideration of complex wind fields and DSD information. Another

involves development for more user-friendly HRPN visualization to support

self-protective action by visitors to Japan and other individuals. Numerous challenges

remain to be tackled; the support of everybody involved is appreciated.

More information: http://www.jma.go.jp/jma/en/Activities/highres_nowcast.html

5. Summary

The high-resolution Precipitation Nowcasts (HRPNs) launched in August 2014 are a

product of the weather radar-based precipitation analysis system that JMA has

operated for more than 30 years. HRPNs are characterized by the comprehensive use

of various observation data types, including information from two distinct radar networks

and from surface-based/upper-air-based observation networks.

The advantages of such comprehensive use include the achievement of more

accurate atmospheric analysis with uncertainty/error information, which contributes to

high-quality prediction as exemplified in HRPN generation. This paper outlines some of

the specific mechanisms of observation data processing used in HRPN generation.

Further information will be shared in the future.

JMA remains committed to its ongoing research and development toward the

improvement of HRPNs via the establishment of highly sophisticated technologies and

integrated observing systems.

Reference

JMA, 2015: Techniques of Precipitation Analysis and Prediction for High-resolution

Precipitation Nowcasts.

http://www.jma.go.jp/jma/en/Activities/Techniques_of_Precipitation_Analysis_and_Pred

iction_developed_for_HRPNs.pdf