code: def output_forecasts_csv(self, forecasts, mode, csv_path, run_date_format="%Y%m%d-%H%M"):
          
          merged_forecasts = pd.merge(forecasts["condition"],
                                      forecasts["dist"],
                                      on=["Step_ID","Track_ID","Ensemble_Member","Forecast_Hour"])
          all_members = self.data[mode]["combo"]["Ensemble_Member"]
          members = np.unique(all_members)
          all_run_dates = pd.DatetimeIndex(self.data[mode]["combo"]["Run_Date"])
          run_dates = pd.DatetimeIndex(np.unique(all_run_dates))
          print(run_dates)
          for member in members:
              for run_date in run_dates:
                  mem_run_index = (all_run_dates == run_date) & (all_members == member)
                  member_forecast = merged_forecasts.loc[mem_run_index]
                  member_forecast.to_csv(join(csv_path, "hail_forecasts_{0}_{1}_{2}.csv".format(self.ensemble_name,
                                                                                                member,
                                                                                                run_date.strftime
  																							  (run_date_format))))
          return
docs: Output hail forecast values to csv files by run date and ensemble member.
  
          Args:
              forecasts:
              mode:
              csv_path:
          Returns: