Weather forecasting and early warning systems, flood forecasting agricultural drought monitoring and forecasting

Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere at a given place and using meteorology to project how the atmosphere will change.

Once calculated by hand based mainly upon changes in barometric pressure, current weather conditions, and sky condition or cloud cover, weather forecasting now relies on computer-based models that take many atmospheric factors into account. Human input is still required to pick the best possible forecast model to base the forecast upon, which involves pattern recognition skills, teleconnections, knowledge of model performance, and knowledge of model biases. The inaccuracy of forecasting is due to the chaotic nature of the atmosphere, the massive computational power required to solve the equations that describe the atmosphere, the error involved in measuring the initial conditions, and an incomplete understanding of atmospheric processes. Hence, forecasts become less accurate as the difference between current time and the time for which the forecast is being made (the range of the forecast) increases. The use of ensembles and model consensus help narrow the error and pick the most likely outcome.

There are a variety of end uses to weather forecasts. Weather warnings are important forecasts because they are used to protect life and property. Forecasts based on temperature and precipitation are important to agriculture, and therefore to traders within commodity markets. Temperature forecasts are used by utility companies to estimate demand over coming days. On an everyday basis, people use weather forecasts to determine what to wear on a given day. Since outdoor activities are severely curtailed by heavy rain, snow and wind chill, forecasts can be used to plan activities around these events, and to plan ahead and survive them.

Modern methods

It was not until the invention of the electric telegraph in 1835 that the modern age of weather forecasting began. Before that, the fastest that distant weather reports could travel was around 100 miles per day (160 km/d), but was more typically 40–75 miles per day (60–120 km/day) (whether by land or by sea). By the late 1840s, the telegraph allowed reports of weather conditions from a wide area to be received almost instantaneously, allowing forecasts to be made from knowledge of weather conditions further upwind.

The two men credited with the birth of forecasting as a science were an officer of the Royal NavyFrancis Beaufort and his protégéRobert FitzRoy. Both were influential men in British naval and governmental circles, and though ridiculed in the press at the time, their work gained scientific credence, was accepted by the Royal Navy, and formed the basis for all of today's weather forecasting knowledge.

Beaufort developed the Wind Force Scale and Weather Notation coding, which he was to use in his journals for the remainder of his life. He also promoted the development of reliable tide tables around British shores, and with his friend William Whewell, expanded weather record-keeping at 200 British Coast guard stations.

Robert FitzRoy was appointed in 1854 as chief of a new department within the Board of Trade to deal with the collection of weather data at sea as a service to mariners. This was the forerunner of the modern Meteorological Office. All ship captains were tasked with collating data on the weather and computing it, with the use of tested instruments that were loaned for this purpose.

A storm in 1859 that caused the loss of the Royal Charter inspired FitzRoy to develop charts to allow predictions to be made, which he called "forecasting the weather", thus coining the term "weather forecast". Fifteen land stations were established to use the telegraph to transmit to him daily reports of weather at set times leading to the first gale warning service. His warning service for shipping was initiated in February 1861, with the use of telegraph communications. The first daily weather forecasts were published in The Times in 1861. In the following year a system was introduced of hoisting storm warning cones at the principal ports when a gale was expected. The "Weather Book"which FitzRoy published in 1863 was far in advance of the scientific opinion of the time.

As the electric telegraph network expanded, allowing for the more rapid dissemination of warnings, a national observational network was developed, which could then be used to provide synoptic analyses. Instruments to continuously record variations in meteorological parameters using photography were supplied to the observing stations from Kew Observatory – these cameras had been invented by Francis Ronaldsin 1845 and his barograph had earlier been used by FitzRoy.

To convey accurate information, it soon became necessary to have a standard vocabulary describing clouds; this was achieved by means of a series of classifications first achieved by Luke Howard in 1802, and standardized in the International Cloud Atlas of 1896.

Numerical prediction

It was not until the 20th century that advances in the understanding of atmospheric physics led to the foundation of modern numerical weather prediction. In 1922, English scientist Lewis Fry Richardsonpublished "Weather Prediction By Numerical Process", after finding notes and derivations he worked on as an ambulance driver in World War I. He described therein how small terms in the prognostic fluid dynamics equations governing atmospheric flow could be neglected, and a finite differencing scheme in time and space could be devised, to allow numerical prediction solutions to be found.      

Richardson envisioned a large auditorium of thousands of people performing the calculations and passing them to others. However, the sheer number of calculations required was too large to be completed without the use of computers, and the size of the grid and time steps led to unrealistic results in deepening systems. It was later found, through numerical analysis, that this was due to numerical instability. The first computerised weather forecast was performed by a team composed of American meteorologists Jule Charney, Philip Thompson, Larry Gates, and Norwegian meteorologist Ragnar Fjørtoft, applied mathematician John von Neumann, and ENIACprogrammer Klara Dan von Neumann. Practical use of numerical weather prediction began in 1955, spurred by the development of programmable electronic computers.

Broadcasts

The first ever daily weather forecasts were published in The Times on August 1, 1861, and the first weather maps were produced later in the same year. In 1911, the Met Office began issuing the first marine weather forecasts via radio transmission. These included gale and storm warnings for areas around Great Britain. In the United States, the first public radio forecasts were made in 1925 by Edward B. "E.B." Rideout, on WEEI, the Edison Electric Illuminating station in Boston. Rideout came from the U.S. Weather Bureau, as did WBZweather forecaster G. Harold Noyes in 1931.

The world's first televised weather forecasts, including the use of weather maps, were experimentally broadcast by the BBC in 1936. This was brought into practice in 1949 after World War II. George Cowling gave the first weather forecast while being televised in front of the map in 1954. In America, experimental television forecasts were made by James C Fidler in Cincinnati in either 1940 or 1947 on the DuMont Television Network. In the late 1970s and early 80s, John Coleman, the first weatherman on ABC-TV's Good Morning America, pioneered the use of on-screen weather satellite information and computer graphics for television forecasts. Coleman was a co-founder of The Weather Channel(TWC) in 1982. TWC is now a 24-hour cable network. Some weather channels have started broadcasting on live broadcasting programs such as YouTube and Periscope to reach more viewers.

How models create forecasts

An example of 500 mbar geopotential height and absolute vorticity prediction from a numerical weather prediction model

The basic idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future. The main inputs from country-based weather services are surface observations from automated weather stations at ground level over land and from weather buoys at sea. The World Meteorological Organization acts to standardize the instrumentation, observing practices and timing of these observations worldwide. Stations either report hourly in METAR reports, or every six hours in SYNOP reports. Sites launch radiosondes, which rise through the depth of the troposphere and well into the stratosphere. Data from weather satellites are used in areas where traditional data sources are not available. Compared with similar data from radiosondes, the satellite data has the advantage of global coverage, however at a lower accuracy and resolution. Meteorological radar provide information on precipitation location and intensity, which can be used to estimate precipitation accumulations over time. Additionally, if a pulse Doppler weather radar is used then wind speed and direction can be determined.

Commerce provides pilot reports along aircraft routes, and ship reports along shipping routes. Research flights using reconnaissance aircraft fly in and around weather systems of interest such as tropical cyclones. Reconnaissance aircraft are also flown over the open oceans during the cold season into systems that cause significant uncertainty in forecast guidance, or are expected to be of high impact 3–7 days into the future over the downstream continent.

Models are initialized using this observed data. The irregularly spaced observations are processed by data assimilationand objective analysis methods, which perform quality control and obtain values at locations usable by the model's mathematical algorithms (usually an evenly spaced grid). The data are then used in the model as the starting point for a forecast. Commonly, the set of equations used to predict the known as the physics and dynamics of the atmosphere are called primitive equations. These equations are initialized from the analysis data and rates of change are determined. The rates of change predict the state of the atmosphere a short time into the future. The equations are then applied to this new atmospheric state to find new rates of change, and these new rates of change predict the atmosphere at a yet further time into the future. This time stepping procedure is continually repeated until the solution reaches the desired forecast time.

The length of the time step chosen within the model is related to the distance between the points on the computational grid, and is chosen to maintain numerical stability. Time steps for global models are on the order of tens of minutes, while time steps for regional models are between one and four minutes. The global models are run at varying times into the future. The Met Office's Unified Model is run six days into the future, the European Centre for Medium-Range Weather Forecasts model is run out to 10 days into the future, while the Global Forecast System model run by the Environmental Modeling Center is run 16 days into the future. The visual output produced by a model solution is known as a prognostic chart, or prog. The raw output is often modified before being presented as the forecast. This can be in the form of statistical techniques to remove known biases in the model, or of adjustment to take into account consensus among other numerical weather forecasts. MOS or model output statistics is a technique used to interpret numerical model output and produce site-specific guidance. This guidance is presented in coded numerical form, and can be obtained for nearly all National Weather Service reporting stations in the United States. As proposed by Edward Lorenz in 1963, long range forecasts, those made at a range of two weeks or more, are impossible to definitively predict the state of the atmosphere, owing to the chaotic nature of the fluid dynamics equations involved. In numerical models, extremely small errors in initial values double roughly every five days for variables such as temperature and wind velocity.

Essentially, a model is a computer program that produces meteorological information for future times at given locations and altitudes. Within any modern model is a set of equations, known as the primitive equations, used to predict the future state of the atmosphere. These equations—along with the ideal gas law—are used to evolve the density, pressure, and potential temperature scalar fields and the velocity vector field of the atmosphere through time. Additional transport equations for pollutants and other aerosols are included in some primitive-equation mesoscale models as well. The equations used are nonlinear partial differential equations, which are impossible to solve exactly through analytical methods, with the exception of a few idealized cases. Therefore, numerical methods obtain approximate solutions. Different models use different solution methods: some global models use spectral methods for the horizontal dimensions and finite difference methods for the vertical dimension, while regional models and other global models usually use finite-difference methods in all three dimensions.

Techniques

Persistence

The simplest method of forecasting the weather, persistence, relies upon today's conditions to forecast the conditions tomorrow. This can be a valid way of forecasting the weather when it is in a steady state, such as during the summer season in the tropics. This method of forecasting strongly depends upon the presence of a stagnant weather pattern. Therefore, when in a fluctuating weather pattern, this method of forecasting becomes inaccurate. It can be useful in both short range forecasts and long range forecasts.

Use of a barometer

Measurements of barometric pressure and the pressure tendency (the change of pressure over time) have been used in forecasting since the late 19th century. The larger the change in pressure, especially if more than 3.5 hPa (2.6 mmHg), the larger the change in weather can be expected. If the pressure drop is rapid, a low pressure system is approaching, and there is a greater chance of rain. Rapid pressure rises are associated with improving weather conditions, such as clearing skies.

Looking at the sky

Marestail shows moisture at high altitude, signalling the later arrival of wet weather.

Along with pressure tendency, the condition of the sky is one of the more important parameters used to forecast weather in mountainous areas. Thickening of cloud cover or the invasion of a higher cloud deck is indicative of rain in the near future. High thin cirrostratus clouds can create halos around the sun or moon, which indicates an approach of a warm front and its associated rain. Morning fog portends fair conditions, as rainy conditions are preceded by wind or clouds that prevent fog formation. The approach of a line of thunderstorms could indicate the approach of a cold front. Cloud-free skies are indicative of fair weather for the near future. A bar can indicate a coming tropical cyclone. The use of sky cover in weather prediction has led to various weather lore over the centuries.

Nowcasting

The forecasting of the weather within the next six hours is often referred to as nowcasting. In this time range it is possible to forecast smaller features such as individual showers and thunderstorms with reasonable accuracy, as well as other features too small to be resolved by a computer model. A human given the latest radar, satellite and observational data will be able to make a better analysis of the small scale features present and so will be able to make a more accurate forecast for the following few hours. However, there are now expert systems using those data and mesoscale numerical model to make better extrapolation, including evolution of those features in time.

Use of forecast models

In the past, the human forecaster was responsible for generating the entire weather forecast based upon available observations. Today, human input is generally confined to choosing a model based on various parameters, such as model biases and performance.  Using a consensus of forecast models, as well as ensemble members of the various models, can help reduce forecast error. However, regardless how small the average error becomes with any individual system, large errors within any particular piece of guidance are still possible on any given model run. Humans are required to interpret the model data into weather forecasts that are understandable to the end user. Humans can use knowledge of local effects that may be too small in size to be resolved by the model to add information to the forecast. While increasing accuracy of forecast models implies that humans may no longer be needed in the forecast process at some point in the future, there is currently still a need for human intervention.

Analog technique

The analog technique is a complex way of making a forecast, requiring the forecaster to remember a previous weather event that is expected to be mimicked by an upcoming event. What makes it a difficult technique to use is that there is rarely a perfect analog for an event in the future. Some call this type of forecasting pattern recognition. It remains a useful method of observing rainfall over data voids such as oceans, as well as the forecasting of precipitation amounts and distribution in the future. A similar technique is used in medium range forecasting, which is known as teleconnections, when systems in other locations are used to help pin down the location of another system within the surrounding regime. An example of teleconnections are by using El Niño-Southern Oscillation (ENSO) related phenomena.

Communicating forecasts to the public

Most end users of forecasts are members of the general public. Thunderstorms can create strong winds and dangerous lightningstrikes that can lead to deaths, power outages, and widespread hail damage. Heavy snow or rain can bring transportation and commerce to a stand-still, as well as cause flooding in low-lying areas. Excessive heat or cold waves can sicken or kill those with inadequate utilities, and drought scan impact water usage and destroy vegetation.

Several countries employ government agencies to provide forecasts and watches/warnings/advisories to the public in order to protect life and property and maintain commercial interests. Knowledge of what the end user needs from a weather forecast must be taken into account to present the information in a useful and understandable way. Examples include the National Oceanic and Atmospheric Administration's National Weather Service(NWS) and Environment Canada's Meteorological Service(MSC). Traditionally, newspaper, television, and radio have been the primary outlets for presenting weather forecast information to the public. In addition, some cities had weather beacons. Increasingly, the internet is being used due to the vast amount of specific information that can be found. In all cases, these outlets update their forecasts on a regular basis.

Severe weather alerts and advisories

A major part of modern weather forecasting is the severe weather alerts and advisories that the national weather services issue in the case that severe or hazardous weather is expected. This is done to protect life and property. Some of the most commonly known of severe weather advisories are the severe thunderstorm and tornado warning, as well as the severe thunderstorm and tornado watch. Other forms of these advisories include winter weather, high wind, flood, tropical cyclone, and fog. Severe weather advisories and alerts are broadcast through the media, including radio, using emergency systems as the Emergency Alert System, which break into regular programming.

Low temperature forecast

The low temperature forecast for the current day is calculated using the lowest temperature found between 7 pm that evening through 7 am the following morning. So, in short, today's forecasted low is most likely tomorrow's low temperature.

Specialist forecasting

There are a number of sectors with their own specific needs for weather forecasts and specialist services are provided to these users.

Air traffic

Because the aviation industry is especially sensitive to the weather, accurate weather forecasting is essential. Fog or exceptionally low ceilings can prevent many aircraft from landing and taking off. Turbulence and icing are also significant in-flight hazards. Thunderstorms are a problem for all aircraft because of severe turbulence due to their updrafts and outflow boundaries, icing due to the heavy precipitation, as well as large hail, strong winds, and lightning, all of which can cause severe damage to an aircraft in flight. Volcanic ash is also a significant problem for aviation, as aircraft can lose engine power within ash clouds. On a day-to-day basis airliners are routed to take advantage of the jet streamtail wind to improve fuel efficiency. Aircrews are briefed prior to takeoff on the conditions to expect en route and at their destination. Additionally, airports often change which runway is being used to take advantage of a headwind. This reduces the distance required for takeoff, and eliminates potential crosswinds.

Marine

Commercial and recreational use of waterways can be limited significantly by wind direction and speed, wave periodicity and heights, tides, and precipitation. These factors can each influence the safety of marine transit. Consequently, a variety of codes have been established to efficiently transmit detailed marine weather forecasts to vessel pilots via radio, for example the MAFOR(marine forecast). Typical weather forecasts can be received at sea through the use of RTTY, Navtex and Radiofax.

Agriculture

Farmers rely on weather forecasts to decide what work to do on any particular day. For example, drying hay is only feasible in dry weather. Prolonged periods of dryness can ruin cotton, wheat, and corncrops. While corn crops can be ruined by drought, their dried remains can be used as a cattle feed substitute in the form of silage. Frosts and freezes play havoc with crops both during the spring and fall. For example, peach trees in full bloom can have their potential peach crop decimated by a spring freeze. Orange groves can suffer significant damage during frosts and freezes, regardless of their timing.

Forestry

Weather forecasting of wind, precipitations and humidity is essential for preventing and controlling wildfires. Different indices, like the Forest fire weather index and the Haines Index, have been developed to predict the areas more at risk to experience fire from natural or human causes. Conditions for the development of harmful insects can be predicted by forecasting the evolution of weather, too.

Utility companies

Electricity and gas companies rely on weather forecasts to anticipate demand, which can be strongly affected by the weather. They use the quantity termed the degree day to determine how strong of a use there will be for heating (heating degree day) or cooling (cooling degree day). These quantities are based on a daily average temperature of 65 °F (18 °C). Cooler temperatures force heating degree days (one per degree Fahrenheit), while warmer temperatures force cooling degree days. In winter, severe cold weather can cause a surge in demand as people turn up their heating. Similarly, in summer a surge in demand can be linked with the increased use of air conditioning systems in hot weather. By anticipating a surge in demand, utility companies can purchase additional supplies of power or natural gas before the price increases, or in some circumstances, supplies are restricted through the use of brownouts and blackouts.

Other commercial companies

Increasingly, private companies pay for weather forecasts tailored to their needs so that they can increase their profits or avoid large losses. For example, supermarket chains may change the stocks on their shelves in anticipation of different consumer spending habits in different weather conditions. Weather forecasts can be used to invest in the commodity market, such as futures in oranges, corn, soybeans, and oil.

Military applications

United Kingdom Armed Forces

Royal Navy

The UK Royal Navy, working with the UK Met Office, has its own specialist branch of weather observers and forecasters, as part of the Hydrographic and Meteorological (HM) specialisation, who monitor and forecast operational conditions across the globe, to provide accurate and timely weather and oceanographic information to submarines, ships and Fleet Air Arm aircraft.

Royal Air Force

A mobile unit in the RAF, working with the UK Met Office, forecasts the weather for regions in which British, allied servicemen and women are deployed. A group based at Camp Bastion provides forecasts for the British armed forces in Afghanistan.

United States Armed Forces

US Navy

Similar to the private sector, military weather forecasters present weather conditions to the war fighter community. Military weather forecasters provide pre-flight and in-flight weather briefs to pilots and provide real time resource protection services for military installations. Naval forecasters cover the waters and ship weather forecasts. The United States Navy provides a special service to both themselves and the rest of the federal government by issuing forecasts for tropical cyclones across the Pacific and Indian Oceans through their Joint Typhoon Warning Center.

US Air Force

Within the United States, Air Force Weather provides weather forecasting for the Air Force and the Army. Air Force forecasters cover air operations in both wartime and peacetime operations and provide Army support; United States Coast Guard marine science technicians provide ship forecasts for ice breakers and other various operations within their realm; and Marine forecasters provide support for ground- and air-based United States Marine Corpsoperations. All four military branches take their initial enlisted meteorology technical training at Keesler Air Force Base. Military and civilian forecasters actively cooperate in analyzing, creating and critiquing weather forecast products.

 

In 1864, two severe cyclonic storms in quick succession hit the east coast of India, causing enormous loss of human lives and property the first one struck Kolkata in October and the second one struck Machilipatnam in November. Concerned with these disasters, the Government appointed a committee in 1865 to formulate a scheme to develop a system of cyclone warnings. On the recommendations of the committee, Kolkata became the first port where a storm warning system was organised in 1865. Thus the issue of storm warning messages started even before the establishment of the Department in 1875. The storm warning scheme for west coast ports (Mumbai, Karachi, Ratnagiri, Vengurla, Karwar and Kumta) came into force in 1880.

 In 1882, besides Kolkata, the ports at Sagar Islands, Mud Port and Diamond Harbour were also included in the list of ports getting storm warning messages. By 1886, the system of early warnings against cyclones was extended to cover all Indian ports.   Upto 1898, two different systems of storm warning signals (one for the east coast ports and another for west coast ports) were in use. As this was leading to some confusion, a uniform system of storm warning signals was introduced at all the Indian ports from 1898. Kolkata office was responsible for issuing storm warning to all the ports (including those of Burma) around the Bay of Bengal, while the west coast ports were served by the Bombay Meteorological Reporter initially and later from Shimla which was then the headquarters of the Department. After the shift of the HQ of the Department from Shimla to Pune in 1928, the storm warning work for west coast was done from Pune. From 1928 till 1945, the storm warning work was managed between the Kolkata and Pune offices for Bay of Bengal and Arabian Sea respectively. 

Cyclone warning is one of the most important functions of the India Meteorological Department and it was the first service undertaken by the Department as early as in 1865 and thus the service started before the establishment of the department in 1875.  

Organisational Structure 

In 1969, the Government of India appointed a committee called the Cyclone Distress Mitigation committee (CDMC) for Andhra Pradesh to examine various measures to mitigate human suffering and reduce loss of life and property due to cyclonic storms. Subsequently similar committees were set up for Orissa and West Bengal. The Cyclone Distress Mitigation committees for Andhra Pradesh and Orissa recommended in 1971-72 that the India Meteorological Department should establish storm warning centres at Visakhapatnam and Bhubaneshwar for issuing cyclone warnings to coastal Andhra Pradesh and coastal Orissa respectively. Consequently, a storm warning centre was set up at Visakhapatnam in 1974, and at Bhubaneshwar in 1973 for catering to the needs of Andhra Pradesh and Orissa respectively.  

In pursuance of the recommendation of Cyclone Review Committee, another Storm Warning Centre was established at Ahmedabad in 1988 for catering the needs of Gujarat, union territory of Diu, Daman, Dadra and Nagar Haveli.   With effect from 1st July 1988 Regional Specialized Meteorological Centre (RSMC) Tropical Cyclones New Delhi has been assigned the responsibility of issuing Tropical Weather Outlooks and Tropical Cyclone Advisories for the benefit of the countries in the WMO/ESCAP Panel region bordering the Bay of Bengal and the Arabian Sea, namely, Bangladesh, Maldives, Myanmar, Oman, Pakistan, Sri Lanka and Thailand. As per one of the recommendations of the Cyclone Review Committee (CRC), a Cyclone Warning Directorate co-located with RSMC Tropical Cyclones New Delhi was established in 1990 in the Office of the Director General of Meteorology, New Delhi to co-ordinate the cyclone warning work in the country in totality. 

ACWCs / CWCs  With the establishment of the additional Centres at Bhubaneshwar and Visakhapatnam, the Storm Warning Centres at Kolkata, Chennai and Mumbai were named as Area Cyclone Warning Centres (ACWC) and the Storm Warning Centres at Visakhapatnam, Bhubaneshwar and Ahmedabad as Cyclone Warning Centres (CWC). CWCs Visakhapatnam, Bhubaneshwar and Ahmedabad function under the control of the ACWCs-Chennai, Kolkata and Mumbai respectively. M. C. Hyderabad liaises between CWC Visakhapatnam and Andhra Pradesh Government officials; warnings issued by CWC Visakhapatnam are sent to M. C. Hyderabad also for briefing the Andhra Pradesh Government officials at the State Capital.  

The present organisational structure for cyclone warnings is a three-tier one, with the ACWCs/CWCs actually performing the operational work of issuing the bulletins and warnings to the various user interests, while the cyclone warnings (Directorate) New Delhi and the Deputy Director General of Meteorology (Weather Forecasting), through Weather Central, Pune coordinates and guides the work of the ACWCs/CWCs, exercises supervision over their work and takes necessary measures for continued improvement and efficiency of the storm warnings system of the country as a whole. The ultimate responsibility for operational storm warning work for the respective areas however, rests with the ACWCs and CWCs.  

Early flood warning system to be tested this monsoon in Chennai

 A comprehensive decision-support system, aimed at mitigating rain-related suffering of Chennai residents, will be put to test during the upcoming monsoon season in the city. The Chennai Flood Early Warning System (CFLOWS), predicting flood inundation scenarios for Chennai, based on a hydrodynamic model with help of forecast and observed rainfall, reservoir and river levels and other parameters from various agencies, has been developed by National Centre for Coastal Research (NCCR), IIT-Mumbai, IIT-Madras and Institute of Remote Sensing-Anna University.

CFLOWS would be able to predict locations at risk of flooding and depth of flooding based on the best forecast products of India Meteorological Department and National Centre for Medium Range Weather Forecasting.

It will disseminate alerts five days in advance. It will be integrated with TNSMART, an application for flood mitigation measures yet to be launched, before being tested this monsoon. “The early warning information generated via CFLOWS will be communicated to revenue officials through TNSMART. Feedback will be received to evaluate alerts through same channel

Community-Based Flood Early-Warning System | India

To enhance the resilience of 45 vulnerable communities in the Indian Himalayan region to flood hazards, a collaboration encompassing ICIMOD, Aranyak and SEE created the Community-Based Flood Early-Warning System. The information and communications technology (ICT) enabled system uses a flood sensor attached to the transmitter to detect rising water levels. When the water reaches a critical level, a signal is wirelessly transmitted to the receiver. The flood warning is then disseminated via mobile phones to appropriate agencies and vulnerable communities downstream. Critical flood levels are set with the help of local communities.

Fast facts:

·         In 2013, five community-based flood early warning systems were installed in the Singora and Jiadhal rivers.

·         The system installed in the Singora River sends flood warning signals to 20 flood-vulnerable communities downstream; 25 flood-vulnerable communities receive warnings from the system installed in the Jiadhal River.

·         During the flood season of 2013, the flood early-warning system installed in the Jiadhal River successfully informed community members of pending floods, helping them save assets and lives.

The problem

The Hindu Kush Himalayan region is one of the most dynamic and complex mountain systems in the world. It is also extremely fragile and sensitive to the effects of climate change. Climate change is gradually increasing the frequency and magnitude of extreme weather events and natural hazards in the region, which has led to higher levels of risk and uncertainty.

One of the effects of climate change is the formation of meltwater lakes on the lower sections of glaciers in the Himalaya region. Because such lakes are inherently unstable and subject to catastrophic flood surges they are potential sources of danger to people and property in the valleys below them.

The solution

The Community-based Flood Early-warning System is an ICT-enabled system to detect and respond to flood emergencies that are prepared and managed by the communities. The wireless system manages flood or flash flood risk by providing early warnings to downstream communities and enhances cooperation between upstream and downstream communities in the sharing of flood information.

This ICT solution consists of two units – a transmitter and a receiver. The transmitter is installed along the riverbank, and the receiver is installed at a house near the river. A flood sensor attached to the transmitter detects rising water levels. When the water reaches a critical level, a signal is wirelessly transmitted to the receiver. The flood warning is then disseminated via mobile phone to concerned agencies and vulnerable communities downstream. Critical flood levels are set with the help of the local community.

Helping people

The system saves lives and property by providing lead time for downstream communities to prepare and respond to the threat of flash floods. It also enhances cooperation between upstream and downstream communities in sharing flood information.

During the 2013 flood season, the Community-based Flood Early-warning System installed in the Jiadhal River successfully informed community members in Dihiri of pending floods, helping them save assets, including cattle and pigs, worth approximately USD 3,300.

Spillover effect

The Community-based Flood Early-warning System project representatives are working with the local government authority to scale up the initiative. This collaboration involves creating district flood management plans and building the capacity of communities to control the system’s equipment. Once the initiative is replicated in other flood-prone areas in the district, the communities can take ownership of the system to sustain it for the long term.

Project representatives have been approached institutions in other countries to help replicate the system in flood-vulnerable areas. In Afghanistan, initial discussions have been held and a partner has been to initiate the process. In Nepal, ICIMOD's initiative on Koshi River Basin Management has included the Community-based Flood Early-warning System methodology in its planning and initial discussions are continuing.

Weather Forecast System 

India Meteorological Department (IMD) operates a dedicated weather and climate monitoring, detection and warning services useful for various sectors of economy. Monsoon prediction and the weather forecasting systems in the country are comparable to the best in the world. However, efforts are continuously being made to further enhance the level of efficiency of the forecasting systems

Improvement of weather forecasting services is a continuous process. Government has initiated a comprehensive modernization programme for IMD covering (i) up-gradation of observation systems (ii) advanced data assimilation tools (iii) advanced communication and IT infrastructure (iv) high performance computing systems and (v) intensive/sophisticated training of IMD personnel. Forecasts, early warning of severe weather events and advisories are issued by IMD at national, state and district levels. In order to provide early warning of severe weather events, IMD has setup a network of State Meteorological Centres to have better coordination with the state and district level agencies. 

To improve the prediction of Monsoon, National Monsoon Mission was launched in 2012. Under the National Monsoon Mission initiative, the Indian Institute of Tropical Meteorology (IITM), Pune, Indian National Centre for Ocean Information Services (INCOIS), Hyderabad and National Centre for Medium Range Weather Forecasting (NCMRWF), Noida have embarked upon to build state-of-the-art coupled ocean atmospheric models for (i) improved prediction of monsoon rainfall on extended range to seasonal time scale (11 days to one season) and (ii) improved prediction of temperature, rainfall and extreme weather events on short to medium range time scale (up to 10 days) so that forecast skill gets quantitatively improved further for the operational services of IMD. 

Through Indo-US collaboration, a “Monsoon Desk” has been set up for working jointly for improving seasonal forecast of Indian monsoon rainfall. Through this forum, Indian and US Scientists are exchanging their ideas and sharing their expertise. This effort has led to appreciable improvements in the efficiency of models in making better forecasts. 

The monsoon forecast for the country is prepared by Climate Prediction Unit of Climate Research and Services Division (CR&S), IMD, Pune. The present long range forecast system based on the statistical models has shown some useful skill in predicting all India seasonal rainfall including the deficient monsoon season rainfall during 2015. However, in order to overcome the limitations of the statistical models used so far, a state of the art dynamical prediction system was implemented by MoES for generating operational long range monsoon forecasts. 

The Gramin Krishi Mausam Seva (GKMS) of IMD has been successful in providing the crop specific advisories to the farmers through different print/visual/Radio/ IT based media including short message service (SMS) and Interactive Voice Response Service (IVRS) facilitating for appropriate field level actions. Weather forecast based agro-meteorological advisories are disseminated through Kisan portal launched by the Ministry of Agriculture and also under public private partner. At present, the GKMS products are disseminated through SMS and IVRS to about 21million farmers in the country. As per the recent National Council of Applied Economic Research (NCAER) report, farming community of the country is using the GKMS service products of India Meteorological Department (IMD) for critical farm operations Viz. (i) Management of sowing (Delayed onset of rains); (ii) Changing crop variety (Delay in rainfall); (iii) Spraying Pesticides for disease control (occurrence of rainfall); (iv) Managing Irrigation (Heavy rainfall Forecast). 

The third party assessment by the National Council of Applied Economic Research (NCAER) on the agromet services provided by the ministry concluded that the annual economic benefit for the farmers cultivating 4 principal crops (Wheat, Rice, Sugarcane and Cotton) was Rs 42,000 Crore in 2015. 

         

An early warning system can be implemented as a chain of information communication systems and comprises sensors, event detection and decision subsystems. They work together to forecast and signal disturbances that adversely affect the stability of the physical world, providing time for the response system to prepare for the adverse event and to minimize its impact.

To be effective, early warning systems need to actively involve the communities at risk, facilitate public education and awareness of risks, effectively disseminate alerts, and warnings and ensure there is constant state of preparedness. A complete and effective early warning system supports four main functions: risk analysis, monitoring and warning; dissemination and communication; and a response capability.

Application

Risk analysis involves systematically collecting data and undertaking risk assessments of predefined hazards and vulnerabilities. Monitoring and warning involves a study of the factors that indicate a disaster is imminent, as well as the methods used to detect these factors. Dissemination and communication concerns communicating the risk information and warnings to reach those in danger in a way that is clear and understandable. Finally, an adequate response capability requires the building of national and community response plan, testing of the plan, and the promotion of readiness to ensure that people know how to respond to warnings.

An early warning system is more than a warning system, which is simply a means by which an alert can be disseminated to the public.

History

Since the Indian Ocean tsunami of 26 December 2004, there has been a surge of interest in developing early warning systems. However, early warning systems can be used to detect a wide range of events, such as vehicular collisions, missile launches, disease outbreaks, and so forth. See warning system for a wider list of applications that can be also be supported by early warning systems.

 

Early warning systems are means by which people receive relevant and timely information in a systematic way prior to a disaster in order to make informed decisions and take action. The word system is used to refer to the interplay between an array of elements aimed at facilitating communication and prompt response to protect and aid those in need.

There are four basic elements to an early warning system where each part must function efficiently for the system to be successful: 

·         Risk knowledge builds the baseline understanding about risks (hazards  and vulnerabilities) and priorities at a given level.

·         Monitoring is the logical follow-on activity to keep up-to-date on how those risks and vulnerabilities change through time.

·         Response capability insists on each level being able to reduce risk once trends are spotted and announced — this may be through pre-season mitigation activities, evacuation or duck-and-cover reflexes, depending on the lead-time of a warning.

·         Warning communication packages the monitoring information into actionable messages understood by those that need, and are prepared, to hear them.

·         An early warning system (EWS) is technology and associated policies and procedures designed to predict and mitigate the harm of natural and human-initiated disasters and other undesirable events.

·         Early warning systems for natural hazards include those designed for floods, earthquakes, avalanches, tsunamis, tornadoes, landslides and drought. Other systems exist for a variety of events including missile launches, road conditions and disease outbreaks.The United Nations' International Strategy for Disaster Reduction (ISDR) recommends that early warning systems have the following four components:

·         Risk knowledge: Data should be systematically collected and analyzed and risk assessments performed.

·         Monitoring and warning service: Systems should be in place to monitor hazards and provide early warning services.

·         Dissemination and communication: Risk information and early warning messages must be delivered.

·         Response capability: Systems should be in place to respond to events.

·         In IT (information technology), early warning systems are used in a variety of environments. The Healthcare Alert Network (HAN) messaging system uses a variety of communication tools, including email, broadcast faxes, television and phone calls, to alert local, state and federal authorities and the media about urgent health threats and necessary actions. The SANS Instituterefers to its Internet Storm Center, which tracks and reports on security threats, as an early warning system for the Internet. Social media analytics software can provide an early warning system for negative customer feedback, such as complaints about products or customer service. Early warning systems for data centers can be used to detect potentially dangerous conditions in the physical environment as well as in the hardware and software systems.

·         Flood forecasting is the use of forecasted precipitation and stream flow data in rainfall-runoff and stream flow routing models to forecast flow rates and water levels for periods ranging from a few hours to days ahead, depending on the size of the watershed or river basin. Flood forecasting can also make use of forecasts of precipitation in an attempt to extend the lead-time available.

·         Flood forecasting is an important component of flood warning, where the distinction between the two is that the outcome of flood forecasting is a set of forecast time-profiles of channel flows or river levels at various locations, while "flood warning" is the task of making use of these forecasts to tell decisions on warnings of floods.

·         Real-time flood forecasting at regional area can be done within seconds by using the technology of artificial neural network. Effective real-time flood forecasting models could be useful for early warning and disaster prevention.

Drought Early Warning System

          A multi-institutional drought early warning system exists in the country, to monitor the behaviour of the agro-climate indicators like rainfall, temperature, reservoirs levels and crop conditions, on a weekly basis from June to September. This early warning system called the ‘Crop Weather Watch Group’, enables the Government to intervene in July- August itself, instead of waiting for an assessment of the damage at the end of the cropping season (October- November). The country has a well-established drought response machinery at the national, state, district and village levels, with institutional mechanisms, to integrate the participation of political and civil society organizations.

From an economic perspective, ‘agricultural drought’ may be viewed as an exogenous, supply-side shock, which is widely recognised as resulting directly in sharp reductions in agricultural production and employment apart from other losses associated with declines in rural income. In addition, meteorological drought, may result in hydrological conditions that have a direct impact on non-agricultural production, including hydro-electric power generation and drinking water supply.

 Flood Warning Systems in India:

Flood forecasting and warning systems in India, consists of structural flood management measures such as embankments and channels, which aim at minimizing flood damage and also better planning of rescue/relief operations. Scientific Flood Forecasting in India is with 173 flood forecasting stations in nine major river systems, and 71 river sub-basins in 15 states. The Central Water Commission (CWC) is in charge of these systems. For other intra-state rivers, states have to establish such systems, usually they are not in place.

Each year, CWC issues nearly 6,000 forecasts during the flood season, usually 12 to 48 hours in advance. For this, CWC has hydrological data from 700 Gauge and Discharge sites and hydro-meteorological data over 500 rain gauge stations, through a network of about 550 wireless stations. IMD provides synoptic weather reports, weather forecast/heavy rainfall warnings etc., to CWC. Flood forecasting systems have received support in each Five year Plan, to improve the systems needed, to issue more accurate and timely warnings.

Cyclone Warning Systems

The India Meteorological Department (IMD), follows a four-stage warning system for issuing warnings for tropical cyclones. A “Pre-cyclone Watch” is issued whenever a depression forms over the Bay of Bengal or Arabian Sea, followed by a “Cyclone Alert”, issued 2-3 days in advance of commencement of bad weather along the coast. In the third stage, Cyclone Warnings are issued 1-2 days in advance, which specify the expected place and time of landfall of the tropical cyclone. The final stage is known as “postlandfall Outlook”, which is issued 12 hours in advanced of landfall and contains location specific forecast of landfall along with other warning details. The Cyclone Warning Organization in India has a 3-tier system to cater to the needs of the maritime States.

These are:

Cyclone Warning Division (CWD) set up at IMD Head Quarters to co-ordinate and supervise cyclone warning operations in the country and to advise the Govt. at the apex level;

§  Area Cyclone Warning Centres (ACWC) at Chennai, Mumbai and Kolkata and Cyclone Warning Centres at Visakhapatnam, Ahmedabad and Bhubaneswar.

The cyclone warning work is also supervised and coordinated by the Forecasting Division at Pune.

With new observation systems such as buoys, Doppler Radars and new generation satellites, these forecasts are likely to improve further. Five Doppler Weather Radars (DWRs) have started functioning along the east coast at Visakapattinam, Kolkata, Machilipatnam, Chennai and Sriharikota.

End-to-End warning system

A system that generates hazard information, constitutes one end and the system that delivers user focused information, to derive a desired response from risk communities, constitutes the other end of the system. Early warning information, effectively generated, communicated and applied, should lead to a change in decisions, that generate improved outcomes in the system of interest. This involves the following elements: the message to be communicated – weather/flood prediction and interpretation into local outlooks; the communication of the message – translation, message construction and dissemination; the receipt of and response to the message; and a feedback mechanism – examining the various aspects of the system with a view to improve its performance. Much more effort also needs to be applied to this end of the implementation chain, and pilot studies are recommended, as an integral part of policy planning.

          There are serious gaps in almost all phases of an early warning system: poor quality and less dense observation equipments, outmoded data analysis, poor prediction and risk assessment practices, non-generation and poor dissemination of user friendly forecast products, both in their content and frequency, and understandably less or no participation of communities, in the early warning system. The gaps in different phases of the early warning system, naturally undermine the effectiveness of the system.

'India may face an intense and increased water deficit next year'

Water deficits will increase and intensify in India in 2019, says the latest edition of Global Water Monitor & Forecast Watch List (November 2018). It represents the regions which are likely to encounter significant water anomalies in the next few months. The results showcase that exceptional water deficits occur throughout Gujarat in the west and severe to exceptional deficits from Madhya Pradesh through Karnataka, as well as in Punjab, Rajasthan, Haryana, and India’s far northeast.

The report presented by IScience (US based limited liability corporation) states the findings from the latest Water Security Indicator Model (WSIM) analysis of global water anomalies using observed temperature and precipitation through October 2018 and an ensemble of forecasts issued the last week of October 2018. ISciences Water Security Indicator Model (WSIM) monitors and forecasts water anomalies on a near global basis. WSIM products include data, visualisations and reports. WSIM includes algorithms to assess the impacts of water anomalies on people, agriculture and electricity generation. WSIM has been run continuously since April 2011 and has been validated against subsequent monitoring based on observed data.

The forecast predicts severe to exceptional surplus water for regions including Jammu and Kashmir, Himachal Pradesh, Uttar Pradesh and Mizoram. Moderate to severe deficits were forecast for Bihar. From February through April, deficits in India are expected to moderate overall and some regions in the country’s eastern third will normalise. However, intense deficits will persist throughout Gujarat and Madhya Pradesh and along the Tungabhadra River through Karnataka. The forecast for the final months — May through July (2019) — indicates primarily moderate deficits in India and pockets throughout the region. Some surpluses are expected in Jammu and Kashmir, northern Pakistan, along the Gandaki River in central Nepal, and pockets of Tamil Nadu.

The 12-month forecast through July 2019 indicates exceptional (greater than 40 years) water deficits in Maharashtra, Telangana, Andhra Pradesh, Karnataka, and Madhya Pradesh.

The previous year datasets are used to derive model value. The results of previous model state that three of the five hottest Septembers on record in India have occurred in the last three years — 2015, 2017, and 2018. Though this September’s extreme heat was unrelated to El Niño — which usually introduces warm dry conditions — El Niño is being blamed for low rainfall during the June-to-September monsoon season. The monsoon rain deficits have caused drought-like conditions in almost a third of Indian districts, and added stress for the farmers.

India’s coffee production is expected to fall to its lowest in five years due to flood damage to plantations in southern states such as Kerala and Karnataka. India exports about three quarters of the coffee it produces, and flood damage has been reported in all key producing areas of the country. The future forecast will help visualise the impact and intensity at a large scale.

It also provides highlights of regional water forecasts for the United States, Canada, Mexico, Central America, South America, Europe, Africa, Middle East, Central Asia and Russia, Southeast Asia and the Pacific East Asia, Australia and New Zealand.