I still have a doubt in classification of why do we need meteorologists while writing answer in 80 to 100 words.
I still have a doubt in classification of why do we need meteorologists while writing answer in 80 to 100 words.
This is an interesting and somewhat controversial question. The mathematical equations that describe atmospheric processes are both not capable of being solved, and incomplete: the atmosphere is connected to many processes that are not currently included in those equations and, even in their incomplete form require the largest and fastest supercomputers to be able to approximate a solution. Moreover, the fact that the equations are not linear means the system of equations is “chaotic” - the results are, among other things, sensitively dependent on the initial conditions we enter (derived from observations, which are imperfect and incomplete). Remarkable progress has been made with numerical weather prediction since it began in the late 1940s, but simulating all of reality is an impossible task. There are many important physical atmospheric processes and interactions that are only poorly simulated, if at all. Another model limitation is its resolution - as the physical scale of any process becomes small enough, it can’t be simulated properly in a model with a given resolution. The models are not, and never will be, perfect. Errors will always creep in and eventually “contaminate” the forecast - the predictability limit for the weather is roughly 10 days or so.
Of course, human forecasts aren’t perfect, either. However, a human can assess the output of a numerical model and when done properly, often can produce an improved forecast by subjective means. Humans can see that the simulation began with a poor set of initial conditions (for various reasons), for an example, and so may be able to account for the poor initialization in light of the known properties of that particular numerical model. Note that there are now several different models operating around the world, and a forecaster can see all of them, using that information to improve the forecast beyond what any single model can do. All of the individual models have unique error characteristics, and a forecaster can use the knowledge of those characteristics to modify and improve upon any forecast based only on a model.
The advantage that models have is they can provide approximate solutions to the approximate mathematical equations used to describe the atmosphere. They take quantitative information and use it in an objective way to produce a forecast. Humans can incorporate non-quantitative information (radar, satellite data, etc.) as well as the model forecasts, so if they are properly educated and trained, meteorologists can equal and sometimes improve the model forecasts. The models predict “ordinary” weather reasonably well, but when the situation becomes unusual, the models can have difficulties and human forecasts then have the capability to make a better forecast product.
This is an interesting and somewhat controversial question. The mathematical equations that describe atmospheric processes are both not capable of being solved, and incomplete: the atmosphere is connected to many processes that are not currently included in those equations and, even in their incomplete form require the largest and fastest supercomputers to be able to approximate a solution. Moreover, the fact that the equations are not linear means the system of equations is “chaotic” - the results are, among other things, sensitively dependent on the initial conditions we enter (derived from observations, which are imperfect and incomplete). Remarkable progress has been made with numerical weather prediction since it began in the late 1940s, but simulating all of reality is an impossible task. There are many important physical atmospheric processes and interactions that are only poorly simulated, if at all. Another model limitation is its resolution - as the physical scale of any process becomes small enough, it can’t be simulated properly in a model with a given resolution. The models are not, and never will be, perfect. Errors will always creep in and eventually “contaminate” the forecast - the predictability limit for the weather is roughly 10 days or so.
Of course, human forecasts aren’t perfect, either. However, a human can assess the output of a numerical model and when done properly, often can produce an improved forecast by subjective means. Humans can see that the simulation began with a poor set of initial conditions (for various reasons), for an example, and so may be able to account for the poor initialization in light of the known properties of that particular numerical model. Note that there are now several different models operating around the world, and a forecaster can see all of them, using that information to improve the forecast beyond what any single model can do. All of the individual models have unique error characteristics, and a forecaster can use the knowledge of those characteristics to modify and improve upon any forecast based only on a model.
The advantage that models have is they can provide approximate solutions to the approximate mathematical equations used to describe the atmosphere. They take quantitative information and use it in an objective way to produce a forecast. Humans can incorporate non-quantitative information (radar, satellite data, etc.) as well as the model forecasts, so if they are properly educated and trained, meteorologists can equal and sometimes improve the model forecasts. The models predict “ordinary” weather reasonably well, but when the situation becomes unusual, the models can have difficulties and human forecasts then have the capability to make a better forecast product.
