SUMMARY METHODS This study implements a deep convolutional neural network with the great potential to recognize patterns of pollen phenomena that enable the prediction of pollen concentrations. The model is trained using data from 2009 to 2015 from multiple meteorological data sets, satellite data, and processed data reflecting pollen flux as input for the model. The model forecasts pollen counts one to seven days ahead for the entire year of 2016. The performance of the algorithm for pollen prediction was evaluated using statistical parameters and categorical statistics evaluation by comparing 1-7 forecast to observation. The algorithm obtains a relatively high index of agreement (0.81-0.90) and Pearson correlation coefficient (0.88-0.75). Evaluation of categorical statistics based on defined threshold levels show satisfactory results. Highlights • Model developed and tested for real-time daily forecast • Model can forecast daily pollen concentrations within minutes • Good accuracy for daily weed and tree pollen forecast for 2016 Pollen data has been measured and acquired from the Houston Department of Health and Human Services (HDHHS) archives. Pollen data contains daily pollen (count/m3) of 25 tree species, 15 weed species and one generalized grass species. We obtained meteorological data from the Texas Commission on Environmental Quality (TCEQ), which operates the Continuous Ambient Monitoring Sites (CAMS) in various metropolitan areas within the state of Texas. We selected data from CAMS station 695 (Moody Tower, near Downtown Houston) for its close proximity to the HDHHS pollen measurement station. Map of Study Area Fig. 1: Map of the study area in Houston. Moody Tower (Red) is the location of collected meteorology data. Pollen Station (Blue) is the location of collected pollen concentration data. A grey area (centered on the Pollen Station) represents the Leaf Area Index (LAI) based on MODIS data for pollen flux calculations. Real-time 7-Day Forecast of Pollen Counts Using Deep Convolutional Neural Network Yannic Lops1, Yunsoo Choi1 (ychoi6@uh.edu), Ebrahim Eslami1, Alqamah Sayeed1 1Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77204, USA RESULTS Acknowledgement: We acknowledge the Texas Commission on Environmental Quality (TCEQ) and Houston Health Department for sharing their data. STUDY AREA RESULTS Deep CNN Forecasting System Fig. 2: Structure of the Deep CNN pollen forecasting system. Input data consist of pollen measurements, meteorological data, and processed data. Processed data consist of: • Meteorological adjustment factor (Ke) Tte, RHte, and WSte represent the threshold values for temperature, relative humidity, and wind speed respectively. C1, C2, and C3 are meteorological resistance factors. • Normal pollen distribution (Ce) Where d is the number of consecutive days which pollen measurements meet or exceed the pollen count, µ is the mean distribution, and σ is the standard deviation. • Characteristic concentration (C*) Where canopy height (hc) is the mean canopy height of the vegetation species. LAI is the computed mean Leaf Area Index from MODIS satellite image data for the respective time period. pq is set as ‘Pollen Count +1’. • Pollen flux (Fp) Where u* is the averaged frictional velocity Deep CNN Model Fig. 3: Simple representation of the Deep CNN model. For evaluation, the categorical statistics evaluation, and pearson correlation coefficient (r) and index of agreement (IOA) statistical evaluation methods are used. Each pollen category and days predicting ahead are evaluated. The categorical statistics evaluation consist of: • Hit Rate (HIT) • Critical Success Index (CSI) • False Alarm Rate (FAR) • Equitable Threat Score (ETS) • Proportion of Correct (POC) Categorical statistic evaluation from 4 quadrants: Na - Predictions above and observations below threshold Nb - Prediction and observation above threshold Nc - Predictions and observations below threshold Nd - Predi