Despite significant advancements in weather forecasting over the past two decades, a winter storm last week served as a stark reminder that predicting Mother Nature is still no easy task.

Winter storms are particularly challenging, often bringing a mix of snow, sleet, freezing rain, and rain -- all in a single event.

It all starts with weather models.

Global models, like the well-known European Model or the National Oceanic and Atmospheric Administration's Global Forecast System, are designed to analyze large-scale weather patterns around the world.

These models help forecasters track the placement of jet streams, major winter storm systems, and cold- versus warm-air masses as they move across the country. They're often the first to signal the possibility of a storm several days in advance -- typically three to seven days out -- and provide an early look at its potential strength.

While global models paint the big picture, mesoscale models deliver high-resolution insights into localized weather outcomes and patterns. These models offer hourly updates and account for regional features like terrain, nearby lakes, and smaller weather fronts.

Typically more useful for short-term forecasts -- ranging from just a few hours to three days before an event -- mesoscale models can more accurately predict snowfall amounts and pinpoint challenging features like heavy snow bands, rain-snow lines, and areas prone to mixed precipitation, including freezing rain and sleet.

The last type of modeling is ensemble forecasting. This approach combines multiple models or makes slight adjustments to a single model to show a range of possible outcomes, allowing forecasters to assess the probability of each scenario.

Simply put, this type of forecasting is very valuable in the winter because it can be used to estimate snowfall ranges, temperature fluctuations, and even the timing of events across both global and mesoscale models.

With so many different models available, forecasts can become difficult and confusing to interpret. This is where meteorologists come in, using a variety of methods to decipher the most likely outcome.

One approach involves comparing forecasts to past events of similar magnitude. History often repeats itself, so patterns from previous storms can help predict how a current event might unfold.

Another strategy is to compare real-time observations to model output. If a particular model has a known bias relevant to the upcoming event or is struggling with current conditions, a meteorologist will factor this into the final forecast.

Additional techniques, such as analyzing weather balloon data, reviewing satellite imagery, and collaborating with other meteorologists, all contribute to a more accurate prediction.

So, why do winter forecasts sometimes miss the mark? The reasons can vary.

In many situations, like earlier this month, models struggle to accurately predict the storm's speed and positioning of mesoscale features like heavy snow bands.

In other instances, slight variations in start and end times can result in snow totals that are several inches off.

Snowflake development also is crucial to snowfall rates and accumulation. Ice crystals and pellets can drastically cut down on snow totals, while much larger, fluffy flakes can boost snowfall.

Sometimes, the challenge is as simple as pinpointing the rain-snow line, as varying temperatures can have a major impact on what falls from the sky.

It's important to remember weather forecasting is not an exact science due to the chaotic nature of the atmosphere.

When watching or reading weather forecasts from your preferred source, pay attention to any uncertainties mentioned. Many National Weather Service offices focus on storm probabilities and address the areas of concern or uncertainty that evolve with time. Any snowfall forecast range comes with a confidence level from the meteorologist who made it.

And finally, in an era where anyone can post an attention-grabbing, inaccurate model run online, rely on trusted sources like the NWS and local meteorologists who spend hours separating fact from fiction.