5.5 Tropical Cyclone forecasting techniques using ground based radar 5.5.1 Introduction In conjunction with information from other sources like satellite-borne weather sensors, ground based synoptic observations and climatology, weather radar data can greatly improve cyclone warning. Strength of weather radar, as a cyclone tracking and prediction tool, lies in its capability to provide accurate, frequent and high-resolution observations even when the cyclones are situated as much as 300 km away providing lead time varying from a few hours to a day or more depending on their position, size, strength, speed and atmospheric conditions. As the cyclones approach closer the fineness and accuracy of data improve further. At range as close as 100 km, accuracy, resolution, and richness of radar data is superior to that from any other source. Unlike conventional radars, present day digital radars with Doppler measurement capabilities can provide additional confirmatory information for centre fixing through features like Radial velocity couplet and wind profiles. High frequency cyclic loop (of the order of one in 5 to 10 minutes) enable good time-lapse animation of system evolution and provide higher confidence in centre fixing and assessing current features of the system in greater detail. Albeit well established, widely accepted and universally used capability of weather radars in precisely detecting and depicting various internal features of weather systems, operationally available prognostic techniques using radar data (per se) is much limited, mostly to extrapolation of position, intensity, motion and evolution. Application of statistical methods [e.g. Centroid tracking applied in many radar product generation software, algorithms used in the nowcasting tool Warning Decision Support System (WDSS) of National Severe Storm Laboratory (NSSL) of USA ] on radar data also yields some handy soft tools for prediction. Owing to high resolution and real-time availability, utility of radar data for setting initial and boundary conditions of Numerical weather Prediction models (NWP) for cyclone prediction, is immense. Hence prognostic value of radar data in cyclone prediction in conjunction with other methods is enormous. 5.5.2 Fixing current position Fixing current position and estimating intensity is the first step in making a track and trend forecast using radar data. Fixing centre of a weak cyclone at relatively farther ranges based on single snapshot image is highly challenging if not impossible. Many a pattern might mislead even an experienced analyst. However animating a number of previous images can reveal the dynamic evolution more clearly. As more sets of radar fixes become available track and trend forecasts can be attempted. Since the forecast quality is dependent on the accuracy of the initial fix considerable care is warranted in this analysis stage. Highly accurate positioning is especially important for short range forecasts in critical situations, such as near landfall, but large position errors have resulted in major forecast failures at all times. The centre of a cyclone is a function both of how we choose to define it and of the type of observing equipment used. For example, satellite and radar are used to locate the geometric centre of a circular region of cloud or rainfall encompassing the eye. Unless there is an exposed surface centre, satellite imagery tends to show the location of the mid-level circulation, which can be quite different from that at the surface in a weakly organized or sheared cyclone. Similarly, radar observations first locate the upper rain features at a

distance, and then progressively sample lower levels as the cyclone approaches. The surface pressure and wind centres are rarely collocated with the geometric centres shown by radar or satellite, or with each other. Weak systems are a particular analysis problem as they may be strongly sheared or contain multiple centres. Which centre to select to represent the actual cyclone is a moot point. During development, one centre may tend to dominate for a period, but then be displaced by a separate centre. Several cases have been found where sharp changes in best tracks have been associated with this changing dominance of multiple centres. There also is a natural tendency for analysts who have carefully considered all aspects and available data to overestimate the accuracy of the derived location. As a tropical cyclone approaches radar the first evidence is usually bands of heavy precipitation, called pre-cyclone squall lines, which move ahead of and at roughly the same speed and direction as the cyclone. Locating the cyclone is not possible at this stage. Once significant lengths of spiral band are observed and the eye is not seen, fitting logarithmic spiral curves with a constant crossing angle of 05-30o can provide an initial indication of the cyclone centre. Higher crossing angles have to be used progressively as we go away from the centre. Examples for curve-fitting window and the track-tool window of radar product generation software IRIS of M/s SIGMET are reproduced in figure 1 & 2 below.

Figure 1 Logarithmic curve fitting example window (IRIS software)

The basic method is to make up an overlay with several logarithmic spiral curves, then to find the best fit to the observed spiral bands.

Figure 2 Track annotate tool using Logarithmic spiral curve fitting ( IRIS software)

The spiral centre then provides the approximate cyclone fix. The likely error margin can vary from a few to tens of km. Logarithmic spiral curve fitting performed manually with conventional radar PPI scopes has lost its relevance with the advent of sophistication in digital image processing and display techniques. Soft overlays or theoretically generated pattern overlaying has evolved as part of display software’s (see figure 1 and 2). Effectiveness of logarithmic spiral curve fitting based on a few spiral bands of cyclones situated in deep seas could have been acceptable. However as the systems approach land mass significant deformation in the shape size and orientation of spiral bands take place with respect to the centre and hence they rarely follow logarithmic spiral curves (figure 3).

Figure 3 Deformation of spiral band orientation close to coast

It is wise not to attempt logarithmic spiral curve fitting on land-falling or close-to-coast systems. (from farther radar scopes). When a closed or partial eye-wall is visible in the reflectivity filed, centre fixing is fairly simple and accurate for all operational forecasting purposes. Reflectivity weighted centre of the closed low-reflectivity region is generally accepted as the cyclone centre. With improved radar sensitivities and capability to display higher resolution in colour displays, the eye is rarely seen as echo-free region. The eye has to be seen as relatively lower reflectivity region within closed higher reflectivity contours. Once an eye or distinct circulation centre appears, radar can provide high frequency locations with errors generally equivalent to those from aircraft reconnaissance (of the order of a few km). The basic technique is to simply find the geometric centre of the eye (which could be circular, elliptic, polygonal or irregular too). However significant analysis errors may occur when the eye is ragged or only formed on one side. In these circumstances, considerable care needs to be taken to find the relative persistence of eye-wall features. This can best be done by looping the PPI images pertaining to several immediate-past-hours, while looking for similar features between successive images and their translatory as well as spinning motion about an imaginary vertical axis. When a closed or nearly closed eye-wall region is not visible, centre fix could be based on empirical method of rough estimate from overall echo patterns and their dynamic evolution and spinning motion, observed through animations. Fix error could be tens of km. As the images from different perspective could be provided to forecaster and analyst at remote locations, the real users are not restricted to the fixes and inferences drawn by the analyst

at the radar station. Viewing the system structure at different CAPPI layers and PPI levels of both reflectivity and velocity fields throw more light on the likely centre than taking decision based on one or two PPI images during the days of conventional radars. CAPPI layers above friction layer are fairly good images to give better idea about the system centre. As the radar beams scan different vertical layers of the cyclone at different ranges, system centre fix is liable to change with the range from the radar due to vertical slantness of the eye-wall. Also the eye-wall diameter has a bearing to the range of observation as the eye-wall widens from bottom to top of the cyclone (funnel shape) - See figure 4.

Figure 4 Funnel shape of eye and asymmetry of eye-wall of cyclone OGNI

5.5.2.1 Centre fixing using velocity features Wood and Brown (1992) concluded the following Doppler velocity characteristics for axisymmetric TCs, which can be applied profitably for centre fix using velocity PPI image. Single-Doppler velocity patterns of an axisymmetric rotation and/or divergence vary as a function of an aspect ratio,

The Doppler velocity maxima rotate counterclockwise (clockwise) as axisymmetric outflow (inflow) superimposed on an axisymmetric rotational flow. The magnitude of the axisymmetric rotational and radial flow can be estimated from the displacement of the extreme Doppler velocities. (Rotation of the whole system about a vertical axis through the vortex centre is a common feature visible in all tropical cyclone animations). The apparent TC centre (defined as the midpoint of the chord connecting two extreme positive and negative Doppler velocity maxima) approaches the true TC centre when α → 0. The true TC center always falls on a circle passing through the radar and two Doppler velocity extremes. As the aspect ratio change from 0 to 2 (i.e. distance from radar to TC Centre change from infinity to core-radius), the apparent centre gradually shifts from true centre to radar location. As the aspect ratio further increases (as radar gets inside the core), the radial component of motion gets lesser and lesser and ultimately when the TC centre and the radar

location coincide the radial velocity field gets filled with near zero values (as the motion all around is perpendicular to the radar beam). However such an ideal situation is rare.

Figure 5 (a) axisymmetric rotation, (b) axisymmetric radial inflow, and (c) axisymmetric rotation and radial inflow. Left panels illustrate the circulation of axisymmetric TCs (gray arrows) and their corresponding Doppler velocities (black arrows). Right panels illustrate the distributions of Doppler velocities vs azimuth angle. (Reproduced from Wood & Brown, 1992) Reflectivity based (both pattern and time-lapse animation based) centre fixing is usually possible for longer period than velocity based centre fixing. However when available, the velocity based fix improves the confidence besides reducing the uncertainty in the fix. 5.5.3 Attempting forecast position

If need be, forecast of the current position using past radar data can be attempted. This forecast may be derived by extrapolation of the track obtained from sufficient number of

past positions taking into account of the previous forecast-position, application of a simple persistence and climatology technique, and/or use of some curve fitting method. The forecast period used depends on the required detail in the operational track. A smooth track for long-period forecasts should use 6-12 hours and a low-order polynomial curve fitting method. Tracks needed to resolve short period oscillations should use a few hours and high-order polynomial fit. For that matter, only radar or regular aircraft fixes are really capable of resolving the details of a trochoidal oscillation. Besides extrapolation, other radar-observed features like orientation and motion of pre-cyclone squall lines, orientation of major/minor axis of elliptical eyes etc. have been tried as predictors of future motion, though these are not very reliable. The past forecast position is the first guess for fixing the current position of the cyclone. This provides a conservative method, especially when using radar data with poorly defined systems, but great care needs to be taken to ensure that the wrong rain feature is not being followed. For hand analysis, the next step is to carefully consider all fixes over the past 24 hours, or so, and indicate those that should be given the highest weighting. Next redraw the track for at least the past 6 to 12 hours using a degree of smoothing appropriate to the situation, as described in the previous paragraph. The next step is to carefully consider the possible errors in the track, particularly those that might significantly change the direction or speed of the immediate past motion, which is of considerable importance for the first 24 hour forecast. It is a good idea to consider all outlying fixes individually. Many can be ruled out as inaccurate, but some may indicate a real change in direction. The basic rule to be applied in such cases is to be conservative and to maintain the current track in uncertain situations. A good rule also is to selectively remove certain fixes of doubtful quality from the track and all computer based systems should have this capacity as a requirement. Occasionally a major analysis error occurs and new information indicates that the cyclone position needs to be substantially modified. In this case, it is a good idea to go back and redraw the track for a substantial period, re-interpreting the observations and their inherent errors in the light of the new information. Considerable care should be taken to understand the reasons for the erroneous track interpretation, especially so that it will not be repeated. Once the analysis position has been decided, it is desirable to give a confidence indicator for the benefit of other users. For example: good 20 km, fair 40 km, or poor 100km. 5.5.4 Intensity determination

Modern radars can provide much valuable information about the intensity of cyclones. Horizontal and vertical distribution of reflectivity and radial velocity help in estimating current intensity of tropical cyclones in terms of rainfall potential and associated wind speed. Time and space derivatives of maximum reflectivity in the eye-wall region and the radius of maximum-reflectivity-ring inside the eye-wall (which also is more or less equal to the radius of maximum wind) provide many means for enumerating trend of tropical cyclones. When dipole structure of radial wind contour around the eye-wall is visible, maximum horizontal wind and associated pressure fall can be worked out using radar data. In other situations, use of radar inputs in numerical models along with other data may however give a solution (Zhao and Jin, 2008). Space time integral of these parameters provide additional information about rainfall, VIL, latent heat energy released, trend

(growth/decay), direction of motion and speed. Features of the cyclone as seen by the radar become prominent as the radar range to the cyclone decreases and vice-versa. There are varying and contrasting observations and evidences relating eye diameter and its time derivative to the cyclone intensity or trend. Statistically, smaller eye size is associated with more intense Tropical Cyclones (TC). In a given TC eye size diminishing with time is usually associated with intensification. However these observations are not universal relations and exceptions are aplenty. In many cases, especially in case of weaker cyclones, tallest and strongest echoes are seen in spiral bands rather than the corresponding eye-wall echoes. Even within the eye-wall region the reflectivity and height variations could be significant. Observations and relations vary with location, time and/or stage of the cyclone. Of all parameters available from a DWR, maximum radial velocity value inside a well captured velocity-couplet signature around the centre of a cyclone is the most accurate and definite parameter quantifying cyclone intensity. This is available only when the cyclone is located not farther than about 200 km from the radar. Beyond that range, resolution of the radar worsens towards the farther side of the cyclone due to beam widening as well as the fact that the lowest level of penetration gets lifted towards the farther end due to earth’s curvature. However when a clear couplet is visible, the maximum radial wind value provides a reasonable estimate of the horizontal wind velocity. This is true because in a circular wind field there are at least two points in which the wind direction is parallel to the observing radar beam. In the vicinity of those two points the component of actual wind in the beam direction happens to be the maximum horizontal wind itself. Hence local maxima contours are likely to be seen around those points, resulting in a couplet shape. As such maximum recorded radial wind in side those contours provide a good estimate of actual maximum wind at that level of the cyclone (See figure 6).

Figure 6 Simulated and observed radial velocity couplets

Even a textual message mentioning the maximum of recorded radial velocity in the vicinity of the eye-wall region help the recipient in not underestimating the wind of destruction, in the absence of data from any other source. While reporting observed maximum radial velocity it is always desirable to mention the height of observation along with the horizontal co-ordinates (either Lat-Long or Azi-Range). Radial velocity field, even within an ill-defined eye-wall, can through some light on the likely intensity of the cyclone and

associated wind field in the sense that the actual wind speed is always more than or equal to the corresponding radial velocity recorded. It is established that the vertical variation of wind inside a tropical cyclone beyond one km is less and surface wind could be about 10% less than the value just above the Planetary Boundary (PB) layer. Hence observed radial velocity field gives a good starting point for estimation of wind of destruction, specifically in not underestimating that. As far as intensity determination is concerned, pressure-defect (the universal measure of cyclone intensity) can be derived from the maximum wind speed. With the advent of DWRs in IMD it has become possible to observe both maximum reflectivity and maximum wind circles around the cyclone simultaneously. Though studies in India (using conventional radars) and USA have shown that the radius of maximum reflectivity is approximately equal to the radius of maximum winds, the postulate could not be verified and established so far in India with Doppler radar data due paucity of sufficient number of suitable cases. It is well known that each cyclone is unique in some way or other. There are narrow-shallow, narrow-deep, wide-shallow, and wide-deep systems, moving towards, away and obliquely through the radar range. Hence each Radar station should fine-tune their scan strategy to optimize the observing system with reference to the cyclone position, stage and trend. It is important to avert velocity folding by selecting suitable unfolding ratio. Variations in the wind profile around the radar, as a cyclone traverses through the radar-range could provide valuable information about the cyclone intensity, vertical extent vertical structure, direction & speed of system movement and much more information. A sample vertical-time-section of wind over Chennai as the cyclone OGNI traversed through its field is shown in figure 7. The vertical time-section of horizontal wind around the radar spanning a period of about 8 hours ( 4 hours prior and 4 fours after) around the time when the system position was closest to the radar, is depicted. Surface wind maximum at 16:34 and 18:34 UTC with a lull in-between and reversal of wind direction (NE to SW) between 17:34 and 18:04 UTC indicate that the maximum-wind ring (eye-wall) crossed the station and the system centre was to the east of radar at the closest point. At 1734 UTC, speed-lull in all levels with direction reversal in vertical (NE at surface to SW beyond 2.4 km, calm at 2.4 km level) indicates close proximity to the system centre. Shallow and narrow nature of the system is also visible in image. Magnitude of overall wind speed and the maximum of about 45 knots at surface speak about the relatively weak intensity of the system.

Figure 7 Vertical time section of wind over radar as the cyclone approach and pass by. 5.5.5 Cyclone track prediction (Direction and speed of motion)

A good conservative method of deriving the operational track is to plot all available fixes. Where possible this should be done on both a computer and a large chart. The chart provides an opportunity for independent hand analysis, a ready reference for later cross checking, and a fail-safe resource in case of a computer failure. The computer provides a capacity for use of objective curve fitting or smoothing routines and ease of direct comparison of tracks derived from different sources. Cyclone could be far away and features as seen by the radar might be faint and ill-defined. Hence predictions based on initial fixes generally prove to be less accurate and with poor degree of confidence. As more and more past position and other details become available, degree of confidence and accuracy of prediction improves. When three or more centre fixes are available direction of motion and speed can be estimated. It is well established that cyclone tracks are seldom straight. They often exhibit a trochoidal motion. A common analysis difficulty arises from the presence of small-scale oscillations in tropical cyclone tracks as seen in figure 8.

Figure 8 Trochoidal oscillation seen in the radar track

When accurate high frequency observations from land-based radar are available, these oscillations can be resolved and could be included in the final track. However (on most occasions the data are insufficient for such detailed analysis) the track is often smoothened to some extent at the forecasting centre and may be biased by the available ad hoc observations. In making an operational fix, it is often nearly impossible to tell whether an analysed position to one side of the previous track is due to poor data, a real oscillation, or a long term change in direction. This can lead to major forecast errors if the wrong interpretation is made. These undulations about a mean long-term track could be attributed to spinning of the centre helically around the mean direction of motion. With time lapsed animation a fair idea about the overall system centre and motion could be visualised and the visual effect provides an indirect benefit of smoothening minute changes through persistence of vision. There are many cases in which clear-cut loops could be seen when images acquired with high temporal resolution (say 1 in10 minutes) are animated. Errors in the centre fix also could contribute to the trochoidal tracks. Hence it is all the more important to consider as

many fixes as available for estimating future course. Software-guided Centroid tracking of individual spiral bands is different from the overall cyclone motion because of prominence of rotary motion of spiral bands about the vortex centre. In fact, motion of spiral bands has little to do with the translatory motion of the TC and hence care should be taken not to blindly rely on such pattern tracking of individual bands. However pattern tracking of the cyclone field as a whole could be used profitably. Radar data processing utilities have provision for interactive tracking of reflectivity patterns in different forms and names. “Interactive Tracking” utility of “Rainbow”, the radar product generation software of M/s Gemetronik is a valuable tool in estimating direction and speed of motion. In this technique, latest image of reflectivity is overlaid with a transparent overlay of reflectivity contour (threshold selectable) of corresponding image pertaining to some previous but relatively recent image. The transparent overlay can be maneuvered around and along the current image to match both patterns as much as possible. During the process, the software computes and displays the direction of motion and its speed corresponding to each intermediate position. Stitched image of different parts of the ITAF product window of Rainbow is given below as figure 9.

Figure 9 ITAF utility demonstrated

5.5.6 Input for surge prediction

Doppler weather radars can provide the most valuable input for storm surge prediction. The maximum wind recorded by a DWR is close to reality when the cyclone is within about 200 km of the observing radar. This information is available at least 4 to 5 Hours before landfall. At this stage, position and magnitude errors are reasonably low and a confident

surge prediction can be provided to the field managers just in time. False alarm rates can be reduced substantially by using velocity and track information from coastal DWR network. Storm surge prediction is done by models or nomograms. Of the inputs needed by the model, radar can give: (1) The maximum wind speed in many cases, which can be converted by a relationship as is

used in the case of satellites, into minimum sea level pressure. (If the observed wind input corresponds to some height above sea level, appropriate scaling down, as per established relations or models, are applied)

(2) The Radius of Maximum Wind, if the Doppler pattern of the vortex is available. If not, the Radius of Maximum Reflectivity from the reflectivity pattern can be used as a proxy.

(3) Expected track and there from the angle of approach to the coast, the time and position of occurrence of the peak surge.

The forecasters use these with other data to produce the storm surge forecast. 5.5.7 Rainfall estimates

No other source of observation can provide the degree of resolution (both spatial and temporal) and promptness available from radars. Spatial distribution of rainfall is as important an input as GIS data of terrain and drainage, for flood and inundation prediction models. Radar generated spatial distribution of precipitation accumulation (time integration of rain-rate) over river-catchments, strategic and residential areas fit the spatial and temporal resolution required for flood prediction models. Heavy rainfall associated with specific features of cyclones (e.g. precyclone squall lines), which come over land well ahead of the storm and severe weather occurring at the rear sector of the cyclones well away from their centre as happens at Mumbai in the case of cyclones hitting Saurashtra are good candidates for radar based surveillance and nowcasting . References Gemetronik Rainbow version 3.7, Operational manual volume 1 Raghavan S., 2003, “Radar Meteorology” Kluwer Academic Publishers, 2003, 564 pp.

ISBN 1-4020-1604-2 Willoughby H.E., 1988, "The dynamics of the tropical cyclone core", Aust. Met. Mag.,

36, 183-191. Wood V.T.,and R.A.Brown, 1992, "Effects of radar proximity on single-Doppler velocity

signatures of axisymmetric rotation and divergence", Mon. Wea. Rev., 120, 2798-2807.

Zhao Q. and Y. Jin, 2008, “High-resolution radar data assimilation for hurricane Isabel

(2003) at landfall”, Bull. Amer. Meteor. Soc., 89, 9, 1355-1372.

Radar, an observational tool for monitoring monsoon 1. Introduction !

Wind and precipitation are the two main criteria deciding the onset, progress, strength and effectiveness of monsoon. Precipitation can be considered the most crucial link between the atmosphere and the earth-surface in weather and climate processes. Quantitative precipitation estimates at high spatial and temporal resolution are of increasing importance for water resource management, for improving the precipitation prediction scores in numerical weather prediction (NWP) models, and for monitoring seasonal to inter-annual climate variability. A dense and high-temporal resolution ground-based measurement network is required to achieve accurate precipitation observations. However, in several regions, especially over the tropical land areas and over the oceans, the coverage by rain gauges is insufficient. Due to the sparse distribution of rain gauges monitoring the movement of the monsoon rains is rather difficult. !

Local economy, hydrology, and ecology in India are heavily dependent on the availability of monsoon rains. Less rainfall during the monsoon season also results in an increased surface albedo (because of decreased soil moisture content), increased dust generation, and less agricultural yield. Thus it is of utmost importance to have a reliable prediction system in place for planning and management of monsoon bounties. Monsoon predictions specifically using computer models require high spatial and temporal distribution of rainfall data for improving their skill-score. Therefore a continuous monitoring of monsoon rain with finer details in space and time is of great importance. Rainfall estimates using ground based radar network as well as space born global precipitation radars do have the potential to provide the degree of resolution and timeliness for improving this monitoring. Apart from these, data from mobile, ship-borne and airborne radars as well as wind profilers do augment the real-time data set available for close and precise monitoring of monsoon activity. Onset and progress of monsoon is indicated by the horizontal profile of wind over the land and neighboring sea area of the country as well as the vertical extend of the wind system. A network of weather radars with the Doppler measurement capability can provide wind field data with high spatial and temporal resolution. Recognising the potential of radars in capturing and depicting details of wind and precipitation fields of monsoon in near real-time, India meteorological department has embarked upon augmenting its radar network as part of its modernisation programme. A radar network covering the entire nation with a reasonable overlap over coastal region is being established. Fig.1 depicts the radar network coverage of IMD [both non-Doppler radars prior to 2001 and the Doppler Radar network being established]. 2. Types of radars in use for monitoring monsoon Based on the operating frequency and purpose of use Weather radars can be classified into different categories. For precipitation estimates S-band radars are the most preferred ones as they can see through heavy precipitation for long distances, without getting attenuated, and provide reliable estimates. However, owing to there huge size and heavy infrastructural constrains they are relatively expensive and hard to maintain. Recent advancement in technology has devised some workarounds for accounting the attenuation effect and made radars operating in the higher frequency bands like C and X-band also provide reliable precipitation estimates, though limited to shorter ranges. Radars used for study of cloud dynamics operate in still higher frequencies even up to Ka and Ku bands, where as wind profilers are operated in HF and VHF bands. Mobile, airborne, and ship borne radars operate mostly in X-band, as heavy radars can’t fit in their limited structural and space constraints. Weather radars are installed on fixed platforms as well us moving platforms like, spacecrafts, ships, Aircrafts and even earth movers like Vans. Space borne precipitation measuring radar under

the famous TRMM (Tropical rain Measuring Mission) of NASA is a befitting example of use of radars in monsoon rain monitoring. 3. Capabilities of typical Doppler weather radar in capturing monsoon parameters Bhatnagar Etal (2003) provides a fairly detailed description of a typical Doppler weather radar and its products. General principles and theory behind radar meteorology can be had from Atlas (1990). Detailed discussions on capabilities & limitations, Choice of frequency, scan strategies, Anamolous propagation echoes, electro magnetic theory of atmospheric sounding, calibration and quality control of the data set etc. can be seen in Doviak & Zrnic (1993) and Reinhearts(1999). 4. Products of Doppler weather radars (DWR) !

A number of products are available from weather radars. From non-Doppler radars, information on reflectivity factor alone is available, whereas from DWRs, in addition to reflectivity, radial velocity and spectrum width information are also available as base data. These data can be used directly for base-product display and also for deriving further products based on standard algorithms. A few products, which are commonly used in operational meteorology with relevance to monsoon monitoring, are briefly described here. !

4.1. Base parameters Reflectivity factor (Z), radial velocity (V) and velocity spectrum width (W) are the three base data directly observed/measured by the radar. The logarithmic radar reflectivity factor is defined as Z = 10 log10 [∑ (NiDi

6)/(1 mm6/m3)], where N is the number of droplets of diameter Di to Di + δD, δD being the diameter interval used in making the measurements, present in unit volume of sample being probed. For the conditions prevailing in most of the weather systems and for the wavelengths used, the scattered power received back is directly proportional to Z (derived by Lord Rayleigh in 1870s). Hence the weather signal power available at the receiver output is a direct measure of Z. Autocorrelation of time series formed by the received power spectrum is the basis for deriving Z and other Doppler moments. The zeroth lag autocorrelation R0 of the time series is proportional to weather signal power, and hence Z is computed from it. Mean radial velocity of hydrometeors inside the sample volume is given by the first lag autocorrelation R1, and the velocity spread inside the sample volume is obtained from the first and the second lag autocorrelations R1 and R2 together, assuming a Gaussian distribution. A point to note while interpreting velocity information available from DWRs is that the radial wind is not the actual prevailing wind, but is the component of the air motion in the direction of the probing radar beam. The base data available from DWRs are generally displayed in the following formats. !

4.2. Radar products for visual interpretation 4.2.1. Plan Position Indication (PPI) In this form of radar image, the parameter pertaining to a constant elevation surface (conical surface with radar as the vertex), is projected on a plain surface to form a radar centric two-dimensional image. The parameter generally displayed in this type of image is anyone of Z, V, W, rain-rate (R) etc.. 4.2.2. Constant Altitude PPI (CAPPI) In this form of radar display, the parameter pertaining to a constant altitude surface (curved surface around the radar and parallel to the earth’s surface) is projected on a plain surface to form a radar centric two-dimensional image. Any of the parameters displayed in PPI can be displayed in CAPPI format also. 4.2.3. Surface rainfall Intensity (SRI)

This form of display is similar to the CAPPI but the parameter displayed is exclusively the rain-intensity over the surface above ground at the selected altitude. This is a derived product in the sense that the measured Z is converted to rainrate using a power regression relation similar to the well known Marshel –Palmer equation Z = 200 R1.6 where Z is the linear radar reflectivity factor in mm6/m3 and R is in mm. 4.2.4. Precipitation Accumulation (PAC) This is another form of visualizing radar derived rainfall quantity. This is a third level product derived by integrating the rain-rate over time. The resulting parameter is the rain accumulation during the period of integration. To have a reliable product, the time interval between rain-rate sampling must be kept as small as possible. 4.2.5. Vertically integrated Liquid water content (VIL) The parameter for this product is derived by converting the Z values to liquid water content and further integrating the same in vertical (using a relation similar to the Marshal-Palmer relation). VIL is displayed in a radar-centric 2-D display. 4.2.6. Vertical Wind Profile All available radial velocity data from a volume scan and within a cylindrical volume of limited radius (within which the assumption that the wind field is linear holds good) are processed together using a multidimensional linear regression method to derive the horizontal wind at different vertical levels. This so-called volume velocity processing (VVP) technique introduced by Waldteufel and Corbin (1979) provide one of the very reliable wind product in the form of vertical time section of wind profile. Optionally the vertical velocity and air-vergence also can be computed and displayed as X-Y plot. 4.2.7. Horizontal wind display From the distribution of radial velocity, which is the component of the air motion in the direction of probing radar beam, prevailing wind in the vicinity of the radar (typically up to a range of about 100 km) is derived using proven algorithms. The derived wind is displayed in a CAPPI like surface using conventional wind barb notation at selected grid points within the selected maximum range. 5. Strength and limitations of DWRs The two major limitations of radar in quantitative estimation of weather parameters are

(i) Inability to see the lower layers of the atmosphere at farther ranges due to the curvature of the earth and

(ii) Poor spatial resolution of data and products at farther ranges due to angular spreading of the sample volume (beam widening).

Owing to the above limitations utility of radar data for quantitative estimates is limited to about 100 to 150 km from the radar. There are many other errors and limitations of varying significance in the radar derived met parameters due to many assumptions and exposure conditions involved in the data acquisition and estimation system. Despite all these limitations, radars continue to occupy a unique position in remote sensing of weather owing to their unparalleled strength in capturing the minute details of small scale variations of weather both in space and time and making the high resolution data available in near real time and at frequent intervals. Radars are also capable of collecting data over ocean and difficult terrains around them, which would have remained unfilled otherwise. It is seen that despite uncertainties and errors involved in the radar based quantitative precipitation estimates, final product shows a good correlation to gauge recorded rainfall. Adjusting radar recorded rainfall using standard data from gauge / disdrometer render the radar data more reliable for both near real-time use as well as for water resource management and flood forecasting

purposes. Continuous operation of weather radars using scan strategies designed for precipitation measurement for an extended period will enable building a detailed hydro-climatology of the region around the radar. Once the envisaged radar network-density is achieved, it will be possible to build the hydro-climatological mapping of the country with spatial resolution down to sub-village level. Such a huge and detailed database will serve as a unique and powerful tool in monitoring of onset, advance, coverage and intensity of monsoon and eventually in monsoon management. !

! !

IMD X-band radar network (2001) IMD S-band radar network( 2001) IMD DWR network being established since 2001

Fig.1 Radar network of IMD – Both Non-Doppler radars prior to 2001 and the Doppler Radars being established !

! Fig. 2 Plan Position Indication of reflectivity

Bright band due to reflection from melting layer is seen Fig. 3 Plan Position Indication of radial Velocity

Westerly wind veering with height is seen !

! Fig. 4 Surface Rainfall Intensity in mm/h Fig. 5 Precipitation Accumulation on a widespread rainy day

!

Fig. 6 Vertical time section of wind given by VVP2 algorithm Fig. 7 Comparison Radar and Gauge reported rainfall over Meenambakkam during NE monsoon 2005

!

! Fig. 8 Vertically integrated Liquid

( 0.1 to 16 km vertically and 100 km horizontally) Fig. 9 Heavy rain over sea (Gauge coverage is nil)

!

! !

Fig.!9!!Horizontal!wind!on!grid!points!–!UWT!algorithm!! Fig.!10!!Radial!wind!on!horizontal!layer!at!1km!

References!

!!

Atlas,!D.,!1990,!Radar!in!Meteorology,!Amer.!Met.!Soc.,!Boston.!

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