Rainfall measurements in tropical ocean regions with navigational radar

Matthias Tomczak, Ivan Lebedev and Aneurin Henry-Edwards
School of Chemistry, Physics and Earth Sciences
The Flinders University of South Australia
GPO Box 2100, Adelaide SA 5001 Australia
matthias.tomczak" + "@" + "flinders.edu.au

A poster presented at the WCRP/SCOR Workshop on Intercomparison and Validation of Ocean-Atmospere Flux Fields in Washington DC, 21 - 24 May 2001


Introduction

Accurate rainfall measurement in the tropics has long been and remains a difficult task. Convective cloud systems, which dominate long periods of tropical rainfall, produce scattered and intermittent rain. It is not uncommon for rain gauges located only a few kilometres apart to register very different rainfall rates. If they are installed in similar terrain and not affected by surrounding topography, their readings converge over time (usually a few weeks). Integration of rainfall intensity over weeks can produce reliable rainfall data at land based observation points, which can then be taken as representative for the regional rainfall.

However, 70% of the earth's surface is covered by ocean, and the ocean of the tropics and subtropics, which between 30°S and 30°N make up 50% of the earth's surface, plays a decisive role in the global freshwater and energy budget. Obviously, some way has to be found to obtain reliable rainfall data over the ocean.

Recognising the need for better rainfall information over the tropical ocean, the global climate research community launched the Tropical Rainfall Measurement Mission (TRMM). When supplied with accurate calibration algorithms this mission can produce reliable global estimates of tropical rainfall. Most of the ground truth data for the algorithms has to come from the ocean.

Ships of opportunity regularly include rainfall data in their voluntary weather reports. The data base created by these volunteer observers cannot be underestimated. But modern ships traverse the tropical oceans in a matter of days. Their rain record is never long enough to allow for integration in time. The work reported here is based on the concept that integration in space at sea can achieve what integration in time achieves on land. Every ship of opportunity carries a navigational radar. If it is possible to extract rainfall information without disturbing the navigational use of the radar, integration over the radar image can potentially provide a reliable measure of regional rainfall on a routine basis.


The First Experiment

We performed two very low budget experiments to investigate the possible use of navigational radar for quantitative rainfall detection.

In August/September 1996 R/V Franklin performed a triangular pattern around a drifting buoy in the eastern Indian Ocean near 2°S, 93°E (Figure 1), towing a Seasoar CTD. Rainfall was measured with an optical rain gauge on the buoy, and on the ship by video taping the radar screen every 10 minutes at 25 frames/s. The Seasoar-derived surface salinity correlates well with the radar-derived rainfall (Figure 2).

Composite images were then constructed from one full rotation of the radar antenna (Figure 3) and converted into digital images. The low resolution threshold of the radar screen allowed only 1/0 discrimination (rain or no rain). Radar clutter in the vicinity of the ship was suppressed and corrected where possible (Figure 4).

Calibration was achieved in two ways.

  1. The radar rainfall observed at the buoy location was compared with the data.
  2. An ocean mixed layer model was driven with observed winds and radar-derived rainfall and the model salinity compared with the observed sea surface salinity. Very good agreement was achieved by adjusting the radar constant C (equivalent rainfall in mm/h for unit pixel value; Figure 5).
Although the ship was never farther afield from the buoy than 10 km, the accumulated rainfall at the two platforms after 1 week differed be a factor of 2. The calibrated radar data produce a rainfall average for a 800 km2 area that falls between the ship and buoy results (Figure 6). Visual evidence, based on a radar image movie, indicates that the buoy managed to avoid most convective systems and therefore severely underestimated the area average rainfall.

Results from this experiment are reported in Lebedev, I. and M. Tomczak (1999) Rainfall measurements with navigational radar, Journal of Geophysical Research 104, 13,697 - 13,708 (1999)


The Second Experiment

In November/December 1999 R/V Franklin performed a regular pattern around two moored buoys in the Gulf of Carpentaria, tropical northern Australia. The buoys were located 35 km apart (Figure 7); both carried optical rain gauges. The antenna output from the ship's radar was downloaded to a PC every 10 minutes for 10 antenna revolutions (30 seconds).

Figure 7 The Second Experiment. Buoy locations are shown as block spots, the shipss track as a square pattern in the centre. The shaded region shows the area covered by the radar.

 

To provide a "first look" facility, the recorded file was immediately processed by averaging all revolutions and displaying the average on the PC screen. Quantitative processing was done after the voyage.

The purpose built data acquisition interface converted the analog radar signal into digital data with 6 bit resolution, allowing for 64 levels of radar intensity. The 10 revolutions were averaged into bins of 1.5° width and 1.2 km length, determined by the technical specifications of the radar.

The averaged data were geo-located, using the ship's heading and position, and interpolated to a rectangular grid with 1 km grid size and north-south/east-west orientation.

Various versions of radar data were compared with various versions of buoy data for calibration: Gaussian averages of radar reflectivity around the buoy positions with radii from 250 m to 1 km in steps of 125 m were correlated with buoy rain gauge data averaged over intervals from 1 minute to 16 minutes in 1 minute increments, where the average was always centred on the time of the corresponding radar observation.

Highest correlation was achieved with a buoy time average of 3 minutes and a Gaussian radar reflectivity average with a radius of 875 m. These parameters were used to determine, through logarithmic correlation, the constants a and b in the relationship Z = α R β between the measured radar reflectivity factor Z and the rain rate R observed at the buoys (Figure 8). Points close to zero (representing radar scatter in the absence of rain) were removed before the correlation.

Unfortunately the period of the experiment coincided with an unusually dry spell in the north Australian monsoon season. The monsoon rains usually arrive in northern Australia in October. During 1999 they had clearly started before mid-November. The buoy moorings were deployed on the 28th of November, and during the first week after deployment reasonable rainfall was received around the Gulf of Carpentaria. The second week of the experiment was extremely dry (Figure 9). The period with useful data was therefore shorter than 1 week. Data analysis is continuing, focussing on comparison with TRMM data (Figure 10).


Discussion and Conclusion

The experiments described here are an attempt at a "proof of concept" for rainfall measurements in the tropical oceans with navigational radar. The results are encouraging. Use of the digital signal from the antenna allows some resolution of rainfall intensity. Figure11 shows examples of processed radar images during the occurrence of convective rainfall. A variation of the rainfall rate across clouds and within cloud systems can clearly be seen. The structure of the cloud systems revealed by the radar is in agreement with similar images seen on dedicated weather radars.

Figure 11 Examples of digitized radar images, showing varying radar reflectivity across convective cloud systems.

 

Navigational radar can of course never achieve the accuracy and resolution of dedicated weather radars. The advantages of navigational radar are

The calibration constants will have to be determined for every radar brand and model, but once they are known they can be used for all radars of that particular type. Initially much rainfall information will thus be uncalibrated and only of qualitative use. After some period the growing global data base will open the possibility of calibrating data through statistical cross-validation. Eventually it should be possible to supply each participating vessel of opportunity with processing software that allows it to regularly and automatically report area-averaged rainfall through satellite transmission.

Appendix: Technical radar details

First Experiment   Second Experiment
radar model JMA-627-7 radar model Furuno
Wavelength 3 cm Antenna slotted waveguide array
20 revolutions per minute
length = 6.5 ft
horizontal beam width = 1.23°
vertical beam width = 25°
attenuation = 26 dB
Polarization horizontal
Rotation ~ 22/minute
Beam width at ­3dB point 1° horizontal
20° vertical
Sidelobe level -26dB within ±10°
-32dB outside ±10°
RF Unit frequency = 9,375 ± 30 MHz
peak output = 20 KW
pulse length = 0.08 and 1.0 ms
pulse repetition rate = 800 Hz
Gain 31dB
Pulse width / PRF (6,12nm) 0.7ms/1000Hz
(24,48nm) 1.0ms/750Hz
Manufacturer Japan Radio Co.
PRF = pulse repetition frequency


© 2000 M. Tomczak
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home page: http://www.es.flinders.edu.au/~mattom
This page last updated 31 May 2001