4 Chapter 4 — Apps: IEM’s Interactive Tools
The Apps menu is where the IEM shifts from archive and network infrastructure to tools you can actually use to answer questions. It contains the tools that produce the charts you see shared on weather forums, the applications that let you explore a station’s full historical record interactively, and several tools that most users never fully discover because the menu entry gives little hint of what lies inside.
This chapter covers all of them, with most of the space devoted to Automated Data Plotting — Autoplot — the single most powerful and underutilized tool on the IEM.
The Application Index
The Application Index at mesonet.agron.iastate.edu/apps.php is the complete catalog of everything IEM has built. It is organized by category and lists well over a hundred individual applications with short descriptions. If you ever find yourself lost on the site wondering whether a tool exists for a specific task, start here. Browse it once in a dedicated session — not to memorize it, but to build a mental map of the categories and where things live. It will save you significant time later.
Automated Data Plotting (Autoplot)
mesonet.agron.iastate.edu/plotting/auto/
Autoplot is the IEM’s chart generation engine. It is a single application with 260 chart types, each capable of producing publication-quality graphics from the IEM’s full data archive. It is the source of the overwhelming majority of charts you will ever see posted from the IEM — temperature records, departure analyses, warning climatologies, MOS verification, snowfall timelines, and dozens of other chart types that simply do not exist anywhere else for free.
Understanding Autoplot requires three things: knowing how to find the right chart, how the URL structure works, and how to export the data.
Finding the Right Chart
The Autoplot interface has a large dropdown selector at the top — Step 1: Select a Chart Type. The full list of 260 charts is in that dropdown, grouped into sections: Daily, Monthly, Yearly, Hydrology/Drought, METAR/ASOS Special Plots, Agricultural, NWS/Warnings, and a miscellaneous section for things that do not fit neatly. Each chart has a name and a number in parentheses.
Below the dropdown, the page shows a visual gallery of thumbnails — one sample image for each chart, organized by the same category groups. This is the fastest way to browse if you know roughly what kind of chart you want but do not know which number. The trending section at the top of the gallery shows which charts are being requested most heavily in the past six hours, which is a useful indicator of what the active weather is prompting people to look at.
When you select a chart from the dropdown or click a thumbnail, the interface loads Step 2: Configure Chart Options. What you see here changes completely depending on which chart you selected. Some charts ask only for a station and a year. Others have a dozen parameters covering network, station, date range, variable, threshold, output format, and display options. All of this configuration happens in the same interface — the form updates dynamically when you change the chart type.
How the URL Works
This is where Autoplot becomes genuinely powerful. Every chart you configure generates a direct URL that can be bookmarked, shared, or used to pull data programmatically. Once you understand the URL structure, you often do not need the dropdown at all — you can construct charts directly in the address bar.
The base URL is https://mesonet.agron.iastate.edu/plotting/auto/ and every individual chart follows the pattern ?q={number} for the interactive interface. The chart image itself — the actual PNG — is served at a different path that encodes all the parameters directly in the URL.
The image URL pattern looks like this:
https://mesonet.agron.iastate.edu/plotting/auto/plot/{q}/{param1}:{value1}::{param2}:{value2}::{param3}:{value3}.pngFor example, chart q=99 (Daily High + Low Temperatures with Departures) for Harrisburg Airport in 2025 would be:
https://mesonet.agron.iastate.edu/plotting/auto/plot/99/network:PACLIMATE::station:PA5703::year:2025::delta:abs::dpi:100.pngThe parameters are separated by :: (double colons) and each parameter is formatted as name:value. The chart number follows /plot/ directly. This URL is stable — if you bookmark it, it will always produce the same chart updated with whatever data the IEM currently has for that station and year.
To change a parameter, change the value in the URL. Want 2024 instead of 2025? Change year:2025 to year:2024. Want sigma departures instead of absolute? Change delta:abs to delta:sigma. This makes Autoplot URLs composable — you can modify any parameter by editing the URL directly, which is far faster than navigating the form when you know what you want.
The _r:t parameter that appears in many Autoplot URLs tells the server to render the page with a data-download link included. You can also retrieve data by replacing .png in the image URL with .csv or .xlsx to get the underlying data directly.
Getting the Data Out
Every Autoplot chart that is based on tabular data — which is most of them — can export that data. On any chart’s page, after the chart is generated, you will see links for CSV Data and Excel Download directly below the image. These export the exact data points used to draw the chart.
This is one of Autoplot’s most underappreciated features. You are not just getting a picture — you are getting a reproducible data pipeline. The IEM calculates the statistics, applies its QC, draws the chart, and then hands you the same numbers in a spreadsheet-ready format. For any analysis that starts with one of these charts, you can pull the data directly rather than downloading raw observations and computing statistics yourself.
The Curated Charts: What a Weather Enthusiast Actually Needs
The 260 charts divide into roughly three groups for weather hobbyist purposes: indispensable tools you will use regularly, useful tools you will pull out for specific questions, and niche tools (mostly agricultural and crop-specific) that you can safely ignore unless you have a specific reason to care.
What follows is a tour of the most valuable charts organized by the kind of question they answer.
Understanding Your Station’s Climate Record
Chart 99 — Daily High + Low Temperatures with Departures The single most-used climatology chart on the IEM. For any given year at any station, it plots every day’s high and low temperature as a colored bar above or below the 1991-2020 NCEI climatological average. Days above average are warm-colored, days below are cool-colored. At a glance you can see which months ran warm or cold, how persistent any anomalies were, and how the year compares to the baseline. The departure can be shown as absolute degrees or as sigma units, which is useful when comparing the same departure across different seasons where variability differs. Link: q=99
Chart 32 — Daily Departures / Percentiles / Ranges for One Year A related chart to q=99 but using period-of-record climatology rather than the fixed 1991-2020 baseline. This one overlays the current year’s observed temperatures against the full envelope of what has been recorded, showing the record high, record low, and shaded percentile bands. Useful when you want to know not just “was it above average” but “how unusual was it by historical standards.” Link: q=32
Chart 113 — Daily Climatology The simple, clean version: the period-of-record normal high, normal low, and average precipitation for every day of the year for a single station. No current year overlay, no departures — just the baseline climatology plotted as smooth curves. This is what you want when you need to explain to someone what “normal” is for a location or when you want a reference chart to annotate with observed values. It accepts a user-defined date range, so you can compute climatology for a specific sub-period rather than the full record. Link: q=113
Chart 100 — Temperature / Precip Statistics by Year One of the most useful charts for long-period analysis. For any station and any variable, it produces a bar chart showing the yearly value across the full period of record, with the annual average overlaid. Want to see every year’s total precipitation since 1888? This is the chart. Want to see how 2012’s drought compares to every prior dry year? This chart ranks them immediately. The breadth of available variables is extensive: annual total precip, annual average temperature, number of days above any temperature threshold, growing degree day totals, and more. Link: q=100
Chart 5 — Daily Records for Each Month of Year Shows, for each month of the year, when the all-time daily records were set. A horizontal bar for each month, with dots indicating which days hold the current records and whether they were set by a more recent year. This is the chart that shows you how old your station’s records are and which months still have records from the early 20th century that have never been broken. Link: q=5
Chart 63 — Records Set by Year The inverse question from q=5: for each year in the record, how many new daily high temperature records, daily low temperature records, and daily precipitation records were set? This chart is revealing when interpreted carefully. Years with many records set are often either extreme weather years or years immediately following a gap in the record (where the newly resumed record can set “records” against a thin baseline). When you see a year like 1936 dominating the high temperature record count, that is real. Link: q=63
Precipitation Analysis
Chart 75 — Precip Totals by Season/Year Simple and essential: total precipitation for each year or season across the full record, with a trend line option. This is your first chart when someone asks “is it getting wetter or drier?” — it puts the full record in context immediately. Link: q=75
Chart 172 — Accumulated Year-to-Date Precipitation For any station, shows the accumulated precipitation from January 1st through whatever date you select, with the current year highlighted against the envelope of all prior years. The wettest and driest years are labeled. This is the chart you want during an ongoing drought or a wet spring — it tells you exactly where the current year sits in the historical distribution at any point in the season. Link: q=172
Chart 108 — Accumulated Departures from Average (Long Term Climate) Plots accumulated growing degree day, precipitation, and stress degree day departures from climatology for a season. The running sum format shows you whether the year is trending wetter or drier as it progresses, and how abrupt the transitions are. This is one of the charts highlighted on the IEM homepage as trending. Link: q=108
Chart 207 — LSR + COOP Snowfall/Rainfall Analysis Maps Produces a gridded analysis map of snowfall or rainfall for any event using Local Storm Reports, NWS COOP data, and CoCoRaHS combined. This is the chart for reconstructing what fell where during a specific storm — the IEM blends three data sources into a single analyzed map. Link: q=207
Departures, Trends, and Comparisons
Chart 55 — Daily Climatology Comparison Compares multiple 30-year climatological normals at the same station on a single plot: the 1971-2000 normals, the 1981-2010 normals, and the 1991-2020 normals, all plotted together. This is the intellectually honest chart for showing climate change signals in the observational record — you can see directly whether the baseline has shifted and by how much. Link: q=55
Chart 215 — Compare Daily Temp Distributions over Two Periods Select two user-defined year ranges and see the full distribution of daily high or low temperatures compared between them as a kernel density estimate. Set period 1 as 1950-1979 and period 2 as 1990-2019 and you can directly visualize whether the tails have shifted. This is the chart for rigorous period-to-period comparison rather than visual inspection of time series. Link: q=215
Chart 128 — Comparison of Yearly Summaries Between Two Stations For any two long-term climate stations, plots their annual summary statistics side by side across all shared years. This is the right tool when analyzing station homogeneity, comparing nearby stations with different exposures, or validating bias correction work. Only years where both stations have adequate data are included. Link: q=128
Chart 104 — Trailing X-Day Temp/Precip Departures (Weather Cycling) Shows the time series of trailing-N-day departures from climatology expressed in sigma units. The result is a plot that looks like a slowly varying signal showing whether the recent past has been persistently warm/cold/wet/dry. Useful for contextualizing current conditions without anchoring to calendar months. Link: q=104
MOS Forecast Verification
Chart 37 — MOS Forecast Ranges + ASOS Observations This chart overlays the archived MOS forecast ranges against what actually occurred at a station. For any ASOS station and any date range, it shows the MOS forecast spread for temperature (or other variables) alongside the observed value, making it easy to see whether observations fell within the forecast range and where systematic biases occurred. This is the gateway chart for the IEM’s MOS verification capabilities and the most relevant Autoplot chart for anyone interested in forecast verification. Link: q=37
Warnings and Severe Weather Climatology
Chart 109 — WFO/State VTEC Event Counts by Period (Map) Generates a map showing the count or coverage of any type of watch, warning, or advisory issued by each WFO or across each state for a user-defined time period. Want to see which WFOs issued the most tornado warnings over the past decade? This chart. Want to see how the warning load shifted during a particular season? This chart. It was the most requested chart on the IEM on the day this guide was verified. Link: q=109
Chart 52 — Gantt Chart of Watch/Warning/Advisories by WFO or UGC Produces a timeline showing when each type of VTEC product was active for a specific WFO or county/zone. The Gantt format makes it easy to see overlapping events, gaps between events, and the seasonal pattern of different warning types across multiple years. Link: q=52
Chart 92 — Days Since Last Watch/Warning/Advisory by WFO A map showing, for each WFO coverage area, how many days have elapsed since the most recent warning of a specific type was issued. This is the “quiet period” chart — useful for contextualizing whether a region is overdue for a particular hazard based on historical frequency. Link: q=92
Chart 44 — NWS Office Accumulated Warning Totals For a specific WFO, plots the accumulated count of severe thunderstorm plus tornado warnings across the season, updated daily, with historical season comparisons overlaid. This is the chart for tracking how active a severe weather season is relative to the historical average at any point during the year. Link: q=44
Hourly and Event-Based Analysis
Chart 43 — Recent Timeseries (Meteogram) A standard meteogram for any ASOS station covering the past 2-3 days: temperature, dew point, wind speed, and precipitation plotted as hourly time series. This is the IEM’s equivalent of the meteogram you see in commercial weather apps, but for any station in the archive. Link: q=43
Chart 87 — Frequency of METAR Code by Week or Hour Shows the annual frequency of any present weather code — thunder, fog, freezing rain, snow, blowing snow — organized as a heatmap by week of year and hour of day. This chart answers questions like “when does fog typically form at my local airport” and “what time of day do thunderstorms most commonly occur at this station across the full record.” Link: q=87
Chart 18 — Long Term Observation Time Series Plots any ASOS variable as a raw time series across the full station record. Want to see every temperature observation ever recorded at your airport plotted in sequence? This chart. It is the most direct view of the raw archive and useful for spotting data quality issues, regime changes, and long-term trends that are visible in the hourly data but get averaged away in daily summaries. Link: q=18
Tips for Using Autoplot Efficiently
A few patterns that make Autoplot faster to use once you know them:
The _r:t URL parameter forces the page to render in a stripped-down format optimized for sharing and embedding. When you copy a chart URL from the address bar, it typically includes this parameter already.
The dpi parameter controls output resolution. The default of 100 DPI produces a sharp web-scale image. For presentations or publications, set dpi:200 or dpi:300 in the URL.
The _fmt:png parameter can be changed to _fmt:pdf or _fmt:svg for different output formats. SVG is particularly useful if you want to edit the chart further in vector graphics software.
Below the generated image, each chart page shows a list of IEM Daily Features that have used that chart type in the past. These are real examples from real weather events, with the “Generate This Chart” link pre-configured. If you are trying to understand what a chart shows, these historical examples are often the fastest way to grasp it.
Workflow 2 — Building and Bookmarking a Custom Autoplot Chart
Question: How does this year’s accumulated precipitation at KMDT compare with the historical average, and how do I save that chart so I can return to it in six months?
Start here: https://mesonet.agron.iastate.edu/plotting/auto/
The Autoplot interface opens with a dropdown selector for chart type and a rendering area below it. The dropdown is organized by category — temperature, precipitation, climatology, NWS/VTEC, and so on. For this example, you want Autoplot q=7, which plots accumulated precipitation departure from average. You can either scroll through the categories to find it or type “7” directly into the chart number field if you know it.
Once you select q=7, the form below the chart selector shows the parameters for that specific chart: network, station, start year, and options for the baseline period. Set the network to PA_ASOS, set the station to MDT, and leave the other parameters at their defaults for a first look. Click the button to generate the chart.
The chart renders as an image. Now look at your browser’s address bar. The URL has changed to something like:
https://mesonet.agron.iastate.edu/plotting/auto/plot/7/network:PA_ASOS::station:MDT::...This URL is the chart. Every parameter you set — network, station, year range, chart type — is encoded in the path. Copy this URL and bookmark it, share it in a forum post, or save it to a spreadsheet. When you load the URL six months from now, IEM will regenerate the chart with the same parameters against the current data. The chart updates automatically; the URL does not need to change.
To download the underlying data rather than the image, replace the /plot/ segment in the URL with /json/, or add _fmt=json to the end of the parameter string. The API will return the chart’s source data as a JSON object — the same values plotted in the chart, available for your own analysis. This works for the majority of Autoplot charts, though a small number of charts produce images that do not have a corresponding data endpoint.
A practical tip for finding the right chart: the Autoplot landing page shows a thumbnail and one-line description for all 260 charts. If you are not sure which chart answers your question, scroll through the thumbnails in the relevant category rather than trying to guess the chart number. The precipitation category and the climatology category together cover most of the questions weather enthusiasts bring to IEM Autoplot.
Workflow 5 — Building a Wind Rose for Your Airport
The question: What does the wind climatology at KMDT look like during winter months? From which direction does the prevailing flow come, and at what speeds?
Start here: https://mesonet.agron.iastate.edu/sites/windrose.phtml
The wind rose tool opens with a network and station selector. Set the network to PA_ASOS and the station to MDT. The date range fields default to a multi-year span; leave them as-is for a first look at the full period of record, which for KMDT extends back to the early 1970s.
The tool offers three ways to filter the data used to build the rose. First, you can restrict to a specific month or season — for a winter wind climatology, set the month filter to December, January, and February. Second, you can restrict to a specific hour of the day, which is useful for understanding diurnal wind patterns or for aviation purposes (morning versus afternoon wind regimes differ significantly at valley airports like MDT due to drainage flow). Third, you can set a start-hour to end-hour window. For a climatological picture of winter winds generally, leave the hour filter open.
You can also specify up to six custom wind speed bins in units of your choice. The default bins work fine for a first look, but if you are interested in a specific threshold — severe wind criteria, aviation minimums, wildfire spread conditions — adjusting the bins to bracket that threshold gives you a more actionable result.
After you click Submit, the tool takes a moment to process: it is querying the full ASOS archive for your station across your time filter, which depending on the date range and station can mean several hundred thousand observations. The site itself notes that generation can take up to a few minutes for large requests; this is normal, not a timeout. The resulting image shows the wind rose with spoke lengths proportional to frequency from each direction and coloring indicating speed category within each directional bin.
The image and the underlying data are both in the public domain. Below the rendered rose you will find a link to the backend data as a JSON file — the raw frequency counts and speed distributions by direction, available for custom plotting or analysis in your own tools. The wind rose image itself can be embedded or shared directly; a stable URL for the plot is generated after the image renders, bookmarkable in the same way as an Autoplot URL.
For KMDT specifically, the winter wind rose will show the dominant west-to-northwest fetch that characterizes cold air advection events in the Susquehanna Valley, with a secondary signature from the south-southwest during pre-frontal warm advection. The MDT wind record is long enough that these patterns are statistically robust; this is not a pattern inferred from only a few seasons of data.